[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-hindupuravinash--the-gan-zoo":3,"tool-hindupuravinash--the-gan-zoo":61},[4,18,26,36,44,52],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",141543,2,"2026-04-06T11:32:54",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107888,"2026-04-06T11:32:50",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":10,"last_commit_at":50,"category_tags":51,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":53,"name":54,"github_repo":55,"description_zh":56,"stars":57,"difficulty_score":10,"last_commit_at":58,"category_tags":59,"status":17},4292,"Deep-Live-Cam","hacksider\u002FDeep-Live-Cam","Deep-Live-Cam 是一款专注于实时换脸与视频生成的开源工具，用户仅需一张静态照片，即可通过“一键操作”实现摄像头画面的即时变脸或制作深度伪造视频。它有效解决了传统换脸技术流程繁琐、对硬件配置要求极高以及难以实时预览的痛点，让高质量的数字内容创作变得触手可及。\n\n这款工具不仅适合开发者和技术研究人员探索算法边界，更因其极简的操作逻辑（仅需三步：选脸、选摄像头、启动），广泛适用于普通用户、内容创作者、设计师及直播主播。无论是为了动画角色定制、服装展示模特替换，还是制作趣味短视频和直播互动，Deep-Live-Cam 都能提供流畅的支持。\n\n其核心技术亮点在于强大的实时处理能力，支持口型遮罩（Mouth Mask）以保留使用者原始的嘴部动作，确保表情自然精准；同时具备“人脸映射”功能，可同时对画面中的多个主体应用不同面孔。此外，项目内置了严格的内容安全过滤机制，自动拦截涉及裸露、暴力等不当素材，并倡导用户在获得授权及明确标注的前提下合规使用，体现了技术发展与伦理责任的平衡。",88924,"2026-04-06T03:28:53",[14,15,13,60],"视频",{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":72,"owner_website":79,"owner_url":80,"languages":81,"stars":86,"forks":87,"last_commit_at":88,"license":89,"difficulty_score":90,"env_os":91,"env_gpu":92,"env_ram":92,"env_deps":93,"category_tags":96,"github_topics":97,"view_count":32,"oss_zip_url":78,"oss_zip_packed_at":78,"status":17,"created_at":101,"updated_at":102,"faqs":103,"releases":134},4475,"hindupuravinash\u002Fthe-gan-zoo","the-gan-zoo","A list of all named GANs!","the-gan-zoo 是一个专注于收集与整理各类生成对抗网络（GAN）变体的开源项目。随着每周都有新的 GAN 论文发表，且研究人员常为模型赋予极具创意甚至晦涩的缩写名称，追踪这一领域的进展变得愈发困难。the-gan-zoo 应运而生，旨在将所有已命名的 GAN 模型汇总成一份清晰的清单，帮助用户快速理清脉络。\n\n该项目不仅提供了按字母顺序排列的模型列表，还附带了详细的论文标题、摘要链接及对应的代码仓库地址。更贴心的是，它支持以表格形式查看数据，允许用户根据发布年份进行筛选或通过标题快速搜索，极大地提升了检索效率。无论是需要调研最新算法的 AI 研究人员、寻找灵感或基准模型的开发者，还是对生成式人工智能感兴趣的设计师与学生，都能从中获益。\n\nthe-gan-zoo 的独特亮点在于其社区驱动的维护模式，欢迎全球贡献者通过提交 Pull Request 来补充遗漏的模型或修正信息，确保知识库的实时性与完整性。作为一个持续更新的“动物园”，它不仅是查阅 GAN 家族成员的便捷索引，更是连接学术界与工程实践的桥梁，让探索纷繁复杂的生成模型世界变得更加轻松有序。","# The GAN Zoo\n\n\u003Cp align=\"center\">\u003Cimg width=\"40%\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhindupuravinash_the-gan-zoo_readme_404db0f30f8c.jpg\" \u002F>\u003C\u002Fp>\n\nEvery week, new GAN papers are coming out and it's hard to keep track of them all, not to mention the incredibly creative ways in which researchers are naming these GANs! So, here's a list of what started as a fun activity compiling all named GANs!\n\n\u003Cp align=\"center\">\u003Cimg width=\"50%\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhindupuravinash_the-gan-zoo_readme_b4ce93351580.jpg\" \u002F>\u003C\u002Fp>\n\nYou can also check out the same data in a tabular format with functionality to filter by year or do a quick search by title [here](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002Fgans.tsv).\n\nContributions are welcome. Add links through pull requests in gans.tsv file in the same format or create an issue to lemme know something I missed or to start a discussion.\n\nCheck out [Deep Hunt](https:\u002F\u002Fdeephunt.in) - my weekly AI newsletter for this repo as [blogpost](https:\u002F\u002Fmedium.com\u002Fdeep-hunt\u002Fthe-gan-zoo-79597dc8c347) and follow me on [Twitter](https:\u002F\u002Fwww.twitter.com\u002Fhindupuravinash).\n\n* 3D-ED-GAN - [Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06375) \n* 3D-GAN - [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.07584) ([github](https:\u002F\u002Fgithub.com\u002Fzck119\u002F3dgan-release))\n* 3D-IWGAN - [Improved Adversarial Systems for 3D Object Generation and Reconstruction](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.09557) ([github](https:\u002F\u002Fgithub.com\u002FEdwardSmith1884\u002F3D-IWGAN))\n* 3D-PhysNet - [3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00328) \n* 3D-RecGAN - [3D Object Reconstruction from a Single Depth View with Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.07969) ([github](https:\u002F\u002Fgithub.com\u002FYang7879\u002F3D-RecGAN))\n* ABC-GAN - [ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F0B3wEP_lEl0laVTdGcHE2VnRiMlE\u002Fview) ([github](https:\u002F\u002Fgithub.com\u002FIgorSusmelj\u002FABC-GAN))\n* ABC-GAN - [GANs for LIFE: Generative Adversarial Networks for Likelihood Free Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11139) \n* AC-GAN - [Conditional Image Synthesis With Auxiliary Classifier GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.09585) \n* acGAN - [Face Aging With Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.01983) \n* ACGAN - [Coverless Information Hiding Based on Generative adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06951) \n* acGAN - [On-line Adaptative Curriculum Learning for GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00020) \n* ACtuAL - [ACtuAL: Actor-Critic Under Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04755) \n* AdaGAN - [AdaGAN: Boosting Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.02386v1) \n* Adaptive GAN - [Customizing an Adversarial Example Generator with Class-Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.10496) \n* AdvEntuRe - [AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.04680) \n* AdvGAN - [Generating adversarial examples with adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.02610) \n* AE-GAN - [AE-GAN: adversarial eliminating with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05474) \n* AE-OT - [Latent Space Optimal Transport for Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05964) \n* AEGAN - [Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10094) \n* AF-DCGAN - [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05347) \n* AffGAN - [Amortised MAP Inference for Image Super-resolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.04490) \n* AIM - [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05972) \n* AL-CGAN - [Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00215) \n* ALI - [Adversarially Learned Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.00704) ([github](https:\u002F\u002Fgithub.com\u002FIshmaelBelghazi\u002FALI))\n* AlignGAN - [AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01400) \n* AlphaGAN - [AlphaGAN: Generative adversarial networks for natural image matting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.10088) \n* AM-GAN - [Activation Maximization Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.02000) \n* AmbientGAN - [AmbientGAN: Generative models from lossy measurements](https:\u002F\u002Fopenreview.net\u002Fforum?id=Hy7fDog0b) ([github](https:\u002F\u002Fgithub.com\u002FAshishBora\u002Fambient-gan))\n* AMC-GAN - [Video Prediction with Appearance and Motion Conditions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.02635) \n* AnoGAN - [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.05921v1) \n* APD - [Adversarial Distillation of Bayesian Neural Network Posteriors](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.10317) \n* APE-GAN - [APE-GAN: Adversarial Perturbation Elimination with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05474) \n* ARAE - [Adversarially Regularized Autoencoders for Generating Discrete Structures](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04223) ([github](https:\u002F\u002Fgithub.com\u002Fjakezhaojb\u002FARAE))\n* ARDA - [Adversarial Representation Learning for Domain Adaptation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01217) \n* ARIGAN - [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00938) \n* ArtGAN - [ArtGAN: Artwork Synthesis with Conditional Categorial GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.03410) \n* ASDL-GAN - [Automatic Steganographic Distortion Learning Using a Generative Adversarial Network](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8017430\u002F) \n* ATA-GAN - [Attention-Aware Generative Adversarial Networks (ATA-GANs)](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.09070) \n* Attention-GAN - [Attention-GAN for Object Transfiguration in Wild Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.06798) \n* AttGAN - [Arbitrary Facial Attribute Editing: Only Change What You Want](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10678) ([github](https:\u002F\u002Fgithub.com\u002FLynnHo\u002FAttGAN-Tensorflow))\n* AttnGAN - [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10485) ([github](https:\u002F\u002Fgithub.com\u002Ftaoxugit\u002FAttnGAN))\n* AVID - [AVID: Adversarial Visual Irregularity Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09521) \n* B-DCGAN - [B-DCGAN:Evaluation of Binarized DCGAN for FPGA](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10930) \n* b-GAN - [Generative Adversarial Nets from a Density Ratio Estimation Perspective](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.02920) \n* BAGAN - [BAGAN: Data Augmentation with Balancing GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09655) \n* Bayesian GAN - [Deep and Hierarchical Implicit Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08896) \n* Bayesian GAN - [Bayesian GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09558) ([github](https:\u002F\u002Fgithub.com\u002Fandrewgordonwilson\u002Fbayesgan\u002F))\n* BCGAN - [Bayesian Conditional Generative Adverserial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05477) \n* BCGAN - [Bidirectional Conditional Generative Adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.07461) \n* BEAM - [Boltzmann Encoded Adversarial Machines](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08682) \n* BEGAN - [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10717) \n* BEGAN-CS - [Escaping from Collapsing Modes in a Constrained Space](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07258) \n* Bellman GAN - [Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01960) \n* BGAN - [Binary Generative Adversarial Networks for Image Retrieval](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.04150) ([github](https:\u002F\u002Fgithub.com\u002Fhtconquer\u002FBGAN))\n* Bi-GAN - [Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.10244) \n* BicycleGAN - [Toward Multimodal Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11586) ([github](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FBicycleGAN))\n* BiGAN - [Adversarial Feature Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.09782v7) \n* BinGAN - [BinGAN: Learning Compact Binary Descriptors with a Regularized GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06778) \n* BourGAN - [BourGAN: Generative Networks with Metric Embeddings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07674) \n* BranchGAN - [Branched Generative Adversarial Networks for Multi-Scale Image Manifold Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08467) \n* BRE - [Improving GAN Training via Binarized Representation Entropy (BRE) Regularization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.03644) ([github](https:\u002F\u002Fgithub.com\u002FBorealisAI\u002Fbre-gan))\n* BridgeGAN - [Generative Adversarial Frontal View to Bird View Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00327) \n* BS-GAN - [Boundary-Seeking Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08431v1) \n* BubGAN - [BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02266) \n* BWGAN - [Banach Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06621) \n* C-GAN  - [Face Aging with Contextual Generative Adversarial Nets ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00237 ) \n* C-RNN-GAN - [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.09904) ([github](https:\u002F\u002Fgithub.com\u002Folofmogren\u002Fc-rnn-gan\u002F))\n* CA-GAN - [Composition-aided Sketch-realistic Portrait Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00899) \n* CaloGAN - [CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02355) ([github](https:\u002F\u002Fgithub.com\u002Fhep-lbdl\u002FCaloGAN))\n* CAN - [CAN: Creative Adversarial Networks, Generating Art by Learning About Styles and Deviating from Style Norms](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.07068) \n* CapsGAN - [CapsGAN: Using Dynamic Routing for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03968) \n* CapsuleGAN - [CapsuleGAN: Generative Adversarial Capsule Network ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06167) \n* CatGAN - [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06390v2) \n* CatGAN - [CatGAN: Coupled Adversarial Transfer for Domain Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08904) \n* CausalGAN - [CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02023) \n* CC-GAN - [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06430) ([github](https:\u002F\u002Fgithub.com\u002Fedenton\u002Fcc-gan))\n* cd-GAN - [Conditional Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00251) \n* CDcGAN - [Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.09105) \n* CE-GAN - [Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04585) \n* CFG-GAN - [Composite Functional Gradient Learning of Generative Adversarial Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.06309) \n* CGAN - [Conditional Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1411.1784) \n* CGAN - [Controllable Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00598) \n* Chekhov GAN - [An Online Learning Approach to Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.03269) \n* ciGAN - [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08093) \n* CinCGAN - [Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00437) \n* CipherGAN - [Unsupervised Cipher Cracking Using Discrete GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.04883) \n* ClusterGAN - [ClusterGAN : Latent Space Clustering in Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.03627) \n* CM-GAN - [CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.05106) \n* CoAtt-GAN - [Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.07613) \n* CoGAN - [Coupled Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.07536v2) \n* ComboGAN - [ComboGAN: Unrestrained Scalability for Image Domain Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06909) ([github](https:\u002F\u002Fgithub.com\u002FAAnoosheh\u002FComboGAN))\n* ConceptGAN - [Learning Compositional Visual Concepts with Mutual Consistency](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06148) \n* Conditional cycleGAN - [Conditional CycleGAN for Attribute Guided Face Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09966) \n* constrast-GAN - [Generative Semantic Manipulation with Contrasting GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00315) \n* Context-RNN-GAN - [Contextual RNN-GANs for Abstract Reasoning Diagram Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.09444) \n* CorrGAN - [Correlated discrete data generation using adversarial training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00925) \n* Coulomb GAN - [Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.08819) \n* Cover-GAN - [Generative Steganography with Kerckhoffs' Principle based on Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04916) \n* cowboy - [Defending Against Adversarial Attacks by Leveraging an Entire GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10652) \n* CR-GAN - [CR-GAN: Learning Complete Representations for Multi-view Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.11191) \n* Cramèr GAN  - [The Cramer Distance as a Solution to Biased Wasserstein Gradients](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10743) \n* Cross-GAN - [Crossing Generative Adversarial Networks for Cross-View Person Re-identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.01760) \n* crVAE-GAN - [Channel-Recurrent Variational Autoencoders](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.03729) \n* CS-GAN - [Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.04887) \n* CSG - [Speech-Driven Expressive Talking Lips with Conditional Sequential Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00154) \n* CT-GAN - [CT-GAN: Conditional Transformation Generative Adversarial Network for Image Attribute Modification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04812) \n* CVAE-GAN - [CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10155) \n* CycleGAN - [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10593) ([github](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FCycleGAN))\n* D-GAN - [Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10267) \n* D-WCGAN - [I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00290) \n* D2GAN - [Dual Discriminator Generative Adversarial Nets](http:\u002F\u002Farxiv.org\u002Fabs\u002F1709.03831) \n* D2IA-GAN - [Tagging like Humans: Diverse and Distinct Image Annotation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00113) \n* DA-GAN  - [DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06454) \n* DADA - [DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00981) \n* DAGAN - [Data Augmentation Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04340) \n* DAN - [Distributional Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09549) \n* DBLRGAN - [Adversarial Spatio-Temporal Learning for Video Deblurring](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00533) \n* DCGAN - [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06434) ([github](https:\u002F\u002Fgithub.com\u002FNewmu\u002Fdcgan_code))\n* DE-GAN - [Generative Adversarial Networks with Decoder-Encoder Output Noise](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03923) \n* DeblurGAN - [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.07064) ([github](https:\u002F\u002Fgithub.com\u002FKupynOrest\u002FDeblurGAN))\n* DeepFD - [Learning to Detect Fake Face Images in the Wild](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.08754) \n* Defense-GAN - [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.06605 ) ([github](https:\u002F\u002Fgithub.com\u002Fkabkabm\u002Fdefensegan))\n* Defo-Net - [Defo-Net: Learning Body Deformation using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05928) \n* DeliGAN - [DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02071) ([github](https:\u002F\u002Fgithub.com\u002Fval-iisc\u002Fdeligan))\n* DF-GAN - [Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04646) \n* DialogWAE - [DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.12352) \n* DiscoGAN - [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.05192v1) \n* DistanceGAN - [One-Sided Unsupervised Domain Mapping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00826) \n* DM-GAN - [Dual Motion GAN for Future-Flow Embedded Video Prediction](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00284) \n* DMGAN - [Disconnected Manifold Learning for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00880) \n* DNA-GAN - [DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05415) \n* DOPING - [DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07632) \n* dp-GAN - [Differentially Private Releasing via Deep Generative Model](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.01594) \n* DP-GAN - [DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.01345 ) \n* DPGAN  - [Differentially Private Generative Adversarial Network ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06739) \n* DR-GAN - [Representation Learning by Rotating Your Faces](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.11136) \n* DRAGAN - [How to Train Your DRAGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07215) ([github](https:\u002F\u002Fgithub.com\u002Fkodalinaveen3\u002FDRAGAN))\n* Dropout-GAN - [Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.11346) \n* DRPAN - [Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09554) \n* DSH-GAN - [Deep Semantic Hashing with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08275) \n* DSP-GAN - [Depth Structure Preserving Scene Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00212) \n* DTLC-GAN - [Generative Adversarial Image Synthesis with Decision Tree Latent Controller](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10603) \n* DTN - [Unsupervised Cross-Domain Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.02200) \n* DTR-GAN - [DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.11228) \n* DualGAN - [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.02510v1) \n* Dualing GAN - [Dualing GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.06216) \n* DVGAN - [Human Motion Modeling using DVGANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.10652) \n* Dynamics Transfer GAN - [Dynamics Transfer GAN: Generating Video by Transferring Arbitrary Temporal Dynamics from a Source Video to a Single Target Image](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.03534) \n* E-GAN - [Evolutionary Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00657) \n* EAR - [Generative Model for Heterogeneous Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.09858) \n* EBGAN - [Energy-based Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.03126v4) \n* ecGAN - [eCommerceGAN : A Generative Adversarial Network for E-commerce](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.03244) \n* ED\u002F\u002FGAN - [Stabilizing Training of Generative Adversarial Networks through Regularization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09367) \n* Editable GAN - [Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.07700) \n* EGAN - [Enhanced Experience Replay Generation for Efficient Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08245) \n* EL-GAN - [EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05525) \n* ELEGANT - [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10562) \n* EnergyWGAN - [Energy-relaxed Wassertein GANs (EnergyWGAN): Towards More Stable and High Resolution Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01026) \n* ESRGAN - [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00219) \n* ExGAN - [Eye In-Painting with Exemplar Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.03999) \n* ExposureGAN - [Exposure: A White-Box Photo Post-Processing Framework](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.09602) ([github](https:\u002F\u002Fgithub.com\u002Fyuanming-hu\u002Fexposure))\n* ExprGAN - [ExprGAN: Facial Expression Editing with Controllable Expression Intensity](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.03842) \n* f-CLSWGAN - [Feature Generating Networks for Zero-Shot Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00981) \n* f-GAN - [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.00709) \n* FairGAN - [FairGAN: Fairness-aware Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11202) \n* Fairness GAN - [Fairness GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09910) \n* FakeGAN - [Detecting Deceptive Reviews using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10364) \n* FBGAN - [Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01694) \n* FBGAN - [Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07862) \n* FC-GAN - [Fast-converging Conditional Generative Adversarial Networks for Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.01972) \n* FF-GAN - [Towards Large-Pose Face Frontalization in the Wild](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.06244) \n* FGGAN - [Adversarial Learning for Fine-grained Image Search](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.02247) \n* Fictitious GAN - [Fictitious GAN: Training GANs with Historical Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08647) \n* FIGAN - [Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06045) \n* Fila-GAN - [Synthesizing Filamentary Structured Images with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02185) \n* First Order GAN  - [First Order Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04591) ([github](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffirst_order_gan))\n* Fisher GAN - [Fisher GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09675) \n* Flow-GAN - [Flow-GAN: Bridging implicit and prescribed learning in generative models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08868) \n* FrankenGAN - [rankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07179) \n* FSEGAN - [Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05747) \n* FTGAN - [Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09618) \n* FusedGAN - [Semi-supervised FusedGAN for Conditional Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.05551) \n* FusionGAN - [Learning to Fuse Music Genres with Generative Adversarial Dual Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01456) \n* FusionGAN - [Generating a Fusion Image: One's Identity and Another's Shape](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.07455) \n* G2-GAN - [Geometry Guided Adversarial Facial Expression Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.03474) \n* GAAN - [Generative Adversarial Autoencoder Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08887) \n* GAF - [Generative Adversarial Forests for Better Conditioned Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.05185) \n* GAGAN - [GAGAN: Geometry-Aware Generative Adverserial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00684) \n* GAIA - [Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.06650) \n* GAIN  - [GAIN: Missing Data Imputation using Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02920) \n* GAMN - [Generative Adversarial Mapping Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.09820) \n* GAN - [Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.2661) ([github](https:\u002F\u002Fgithub.com\u002Fgoodfeli\u002Fadversarial))\n* GAN Lab - [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.01587) \n* GAN Q-learning - [GAN Q-learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.04874) \n* GAN-AD - [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04758) \n* GAN-ATV - [A Novel Approach to Artistic Textual Visualization via GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10553) \n* GAN-CLS - [Generative Adversarial Text to Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.05396) ([github](https:\u002F\u002Fgithub.com\u002Freedscot\u002Ficml2016))\n* GAN-RS - [Towards Qualitative Advancement of Underwater Machine Vision with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00736) \n* GAN-SD - [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10000) \n* GAN-sep - [GANs for Biological Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.04692) ([github](https:\u002F\u002Fgithub.com\u002Faosokin\u002Fbiogans))\n* GAN-VFS - [Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02681) \n* GAN-Word2Vec - [Adversarial Training of Word2Vec for Basket Completion](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08720) \n* GANAX - [GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01107) \n* GANCS - [Deep Generative Adversarial Networks for Compressed Sensing Automates MRI](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00051) \n* GANDI - [Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.01391) \n* GANG - [GANGs: Generative Adversarial Network Games](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00679) \n* GANG - [Beyond Local Nash Equilibria for Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07268) \n* GANosaic - [GANosaic: Mosaic Creation with Generative Texture Manifolds](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00269) \n* GANVO - [GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05786) \n* GAP - [Context-Aware Generative Adversarial Privacy](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09549) \n* GAP - [Generative Adversarial Privacy](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.05306) \n* GATS - [Sample-Efficient Deep RL with Generative Adversarial Tree Search](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05780) \n* GAWWN - [Learning What and Where to Draw](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.02454) ([github](https:\u002F\u002Fgithub.com\u002Freedscot\u002Fnips2016))\n* GC-GAN - [Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.01822 ) \n* GcGAN - [Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05852) \n* GeneGAN - [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.04932) ([github](https:\u002F\u002Fgithub.com\u002FPrinsphield\u002FGeneGAN))\n* GeoGAN - [Generating Instance Segmentation Annotation by Geometry-guided GAN ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.08839 ) \n* Geometric GAN - [Geometric GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02894) \n* GIN - [Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04495) \n* GLCA-GAN - [Global and Local Consistent Age Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.08390) \n* GM-GAN - [Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.10356) \n* GMAN - [Generative Multi-Adversarial Networks](http:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01673) \n* GMM-GAN - [Towards Understanding the Dynamics of Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09884) \n* GoGAN - [Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04865) \n* GONet - [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.03254) \n* GP-GAN - [GP-GAN: Towards Realistic High-Resolution Image Blending](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.07195) ([github](https:\u002F\u002Fgithub.com\u002Fwuhuikai\u002FGP-GAN))\n* GP-GAN - [GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.00962) \n* GPU - [A generative adversarial framework for positive-unlabeled classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08054) \n* GRAN - [Generating images with recurrent adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.05110) ([github](https:\u002F\u002Fgithub.com\u002Fjiwoongim\u002FGRAN))\n* Graphical-GAN - [Graphical Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03429) \n* GraphSGAN - [Semi-supervised Learning on Graphs with Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00130) \n* GraspGAN - [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.07857) \n* GT-GAN - [Deep Graph Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09980) \n* HAN - [Chinese Typeface Transformation with Hierarchical Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06448) \n* HAN - [Bidirectional Learning for Robust Neural Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08006) \n* HiGAN - [Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.04384) \n* HP-GAN - [HP-GAN: Probabilistic 3D human motion prediction via GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09561) \n* HR-DCGAN - [High-Resolution Deep Convolutional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06491) \n* hredGAN - [Multi-turn Dialogue Response Generation in an Adversarial Learning framework](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11752) \n* IAN - [Neural Photo Editing with Introspective Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.07093) ([github](https:\u002F\u002Fgithub.com\u002Fajbrock\u002FNeural-Photo-Editor))\n* IcGAN - [Invertible Conditional GANs for image editing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06355) ([github](https:\u002F\u002Fgithub.com\u002FGuim3\u002FIcGAN))\n* ID-CGAN - [Image De-raining Using a Conditional Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.05957v3) \n* IdCycleGAN - [Face Translation between Images and Videos using Identity-aware CycleGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00971) \n* IFcVAEGAN - [Conditional Autoencoders with Adversarial Information Factorization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05175) \n* iGAN - [Generative Visual Manipulation on the Natural Image Manifold](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.03552v2) ([github](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FiGAN))\n* IGMM-GAN - [Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02728) \n* Improved GAN - [Improved Techniques for Training GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.03498) ([github](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fimproved-gan))\n* In2I - [In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09334) \n* InfoGAN - [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.03657v1) ([github](https:\u002F\u002Fgithub.com\u002Fopenai\u002FInfoGAN))\n* IntroVAE - [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.06358) \n* IR2VI - [IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.09565) \n* IRGAN - [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10513v1) \n* IRGAN - [Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03577) \n* ISGAN - [Invisible Steganography via Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08571) \n* ISP-GPM - [Inner Space Preserving Generative Pose Machine](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.02104) \n* Iterative-GAN - [Two Birds with One Stone: Iteratively Learn Facial Attributes with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06078) ([github](https:\u002F\u002Fgithub.com\u002Fpunkcure\u002FIterative-GAN))\n* IterGAN - [IterGANs: Iterative GANs to Learn and Control 3D Object Transformation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05651) \n* IVE-GAN - [IVE-GAN: Invariant Encoding Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08646) \n* iVGAN - [Towards an Understanding of Our World by GANing Videos in the Wild](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11453) ([github](https:\u002F\u002Fgithub.com\u002Fbernhard2202\u002Fimproved-video-gan))\n* IWGAN - [On Unifying Deep Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00550) \n* JointGAN - [JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02978) \n* JR-GAN - [JR-GAN: Jacobian Regularization for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.09235) \n* KBGAN - [KBGAN: Adversarial Learning for Knowledge Graph Embeddings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04071) \n* KGAN - [KGAN: How to Break The Minimax Game in GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.01744) \n* l-GAN - [Representation Learning and Adversarial Generation of 3D Point Clouds](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.02392) \n* LAC-GAN - [Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.05096) \n* LAGAN - [Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.05927) \n* LAPGAN - [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.05751) ([github](https:\u002F\u002Fgithub.com\u002Ffacebook\u002Feyescream))\n* LB-GAN - [Load Balanced GANs for Multi-view Face Image Synthesis](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.07447) \n* LBT - [Learning Implicit Generative Models by Teaching Explicit Ones](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03870) \n* LCC-GAN - [Adversarial Learning with Local Coordinate Coding](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.04895) \n* LD-GAN - [Linear Discriminant Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.07831) \n* LDAN - [Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.01993) \n* LeakGAN - [Long Text Generation via Adversarial Training with Leaked Information](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.08624) \n* LeGAN - [Likelihood Estimation for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.07530) \n* LGAN - [Global versus Localized Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06020) \n* Lipizzaner - [Towards Distributed Coevolutionary GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08194) \n* LR-GAN - [LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.01560v1) \n* LS-GAN - [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.06264) \n* LSGAN - [Least Squares Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.04076v3) \n* M-AAE - [Mask-aware Photorealistic Face Attribute Manipulation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08882) \n* MAD-GAN - [Multi-Agent Diverse Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.02906) \n* MAGAN - [MAGAN: Margin Adaptation for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.03817v1) \n* MAGAN - [MAGAN: Aligning Biological Manifolds](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00385) \n* MalGAN - [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.05983v1) \n* MaliGAN - [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07983) \n* manifold-WGAN - [Manifold-valued Image Generation with Wasserstein Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01551) \n* MARTA-GAN - [Deep Unsupervised Representation Learning for Remote Sensing Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.08879) \n* MaskGAN - [MaskGAN: Better Text Generation via Filling in the ______ ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.07736 ) \n* MC-GAN - [Multi-Content GAN for Few-Shot Font Style Transfer](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00516) ([github](https:\u002F\u002Fgithub.com\u002Fazadis\u002FMC-GAN))\n* MC-GAN - [MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.01123) \n* McGAN - [McGan: Mean and Covariance Feature Matching GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08398v1) \n* MD-GAN - [Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.07592) \n* MDGAN - [Mode Regularized Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.02136) \n* MedGAN - [Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.06490v1) \n* MedGAN - [MedGAN: Medical Image Translation using GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06397) \n* MEGAN - [MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02481) \n* MelanoGAN - [MelanoGANs: High Resolution Skin Lesion Synthesis with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04338) \n* memoryGAN - [Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01500) \n* MeRGAN - [Memory Replay GANs: learning to generate images from new categories without forgetting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02058) \n* MGAN - [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.04382) ([github](https:\u002F\u002Fgithub.com\u002Fchuanli11\u002FMGANs))\n* MGGAN - [Multi-Generator Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02556) \n* MGGAN - [MGGAN: Solving Mode Collapse using Manifold Guided Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04391) \n* MIL-GAN - [Multimodal Storytelling via Generative Adversarial Imitation Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01455) \n* MinLGAN - [Anomaly Detection via Minimum Likelihood Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00200) \n* MIX+GAN - [Generalization and Equilibrium in Generative Adversarial Nets (GANs)](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00573v3) \n* MIXGAN - [MIXGAN: Learning Concepts from Different Domains for Mixture Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.01659) \n* MLGAN - [Metric Learning-based Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02792) \n* MMC-GAN - [A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks from Ambiguous Language Instructions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03847) \n* MMD-GAN - [MMD GAN: Towards Deeper Understanding of Moment Matching Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08584) ([github](https:\u002F\u002Fgithub.com\u002Fdougalsutherland\u002Fopt-mmd))\n* MMGAN - [MMGAN: Manifold Matching Generative Adversarial Network for Generating Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08273) \n* MoCoGAN - [MoCoGAN: Decomposing Motion and Content for Video Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.04993) ([github](https:\u002F\u002Fgithub.com\u002Fsergeytulyakov\u002Fmocogan))\n* Modified GAN-CLS - [Generate the corresponding Image from Text Description using Modified GAN-CLS Algorithm](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.11302) \n* ModularGAN - [Modular Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03343) \n* MolGAN - [MolGAN: An implicit generative model for small molecular graphs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11973) \n* MPM-GAN - [Message Passing Multi-Agent GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.01294) \n* MS-GAN - [Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7014-temporal-coherency-based-criteria-for-predicting-video-frames-using-deep-multi-stage-generative-adversarial-networks) \n* MTGAN - [MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09059) \n* MuseGAN - [MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.06298) \n* MV-BiGAN - [Multi-view Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.02019v1) \n* N2RPP - [N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02825) \n* NAN - [Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03287) \n* NCE-GAN - [Dihedral angle prediction using generative adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10996) \n* ND-GAN - [Novelty Detection with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10560) \n* NetGAN - [NetGAN: Generating Graphs via Random Walks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00816) \n* OCAN - [One-Class Adversarial Nets for Fraud Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01798) \n* OptionGAN - [OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.06683) \n* ORGAN - [Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10843) \n* ORGAN - [3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversary Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06363) \n* OT-GAN - [Improving GANs Using Optimal Transport](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.05573) \n* PacGAN - [PacGAN: The power of two samples in generative adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04086) \n* PAN - [Perceptual Adversarial Networks for Image-to-Image Transformation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09138) \n* PassGAN - [PassGAN: A Deep Learning Approach for Password Guessing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00440) \n* PD-WGAN - [Primal-Dual Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09575) \n* Perceptual GAN - [Perceptual Generative Adversarial Networks for Small Object Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05274) \n* PGAN - [Probabilistic Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01886) \n* PGD-GAN - [Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.08406) \n* PGGAN - [Patch-Based Image Inpainting with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.07422) \n* PIONEER - [Pioneer Networks: Progressively Growing Generative Autoencoder](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03026) \n* Pip-GAN - [Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10742) \n* pix2pix - [Image-to-Image Translation with Conditional Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.07004) ([github](https:\u002F\u002Fgithub.com\u002Fphillipi\u002Fpix2pix))\n* pix2pixHD - [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11585) ([github](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpix2pixHD))\n* PixelGAN - [PixelGAN Autoencoders](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00531) \n* PM-GAN - [PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.06248) \n* PN-GAN - [Pose-Normalized Image Generation for Person Re-identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02225) \n* POGAN - [Perceptually Optimized Generative Adversarial Network for Single Image Dehazing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.01084) \n* Pose-GAN - [The Pose Knows: Video Forecasting by Generating Pose Futures](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.00053) \n* PP-GAN - [Privacy-Protective-GAN for Face De-identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.08906) \n* PPAN - [Privacy-Preserving Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07008) \n* PPGN - [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00005) \n* PrGAN - [3D Shape Induction from 2D Views of Multiple Objects](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05872) \n* ProGanSR - [A Fully Progressive Approach to Single-Image Super-Resolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02900) \n* Progressive GAN - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10196) ([github](https:\u002F\u002Fgithub.com\u002Ftkarras\u002Fprogressive_growing_of_gans))\n* PS-GAN - [Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02047) \n* PSGAN - [Learning Texture Manifolds with the Periodic Spatial GAN](http:\u002F\u002Farxiv.org\u002Fabs\u002F1705.06566) \n* PSGAN - [PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.03371) \n* PS²-GAN - [High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10182) \n* RadialGAN - [RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06403) \n* RaGAN - [The relativistic discriminator: a key element missing from standard GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00734) \n* RAN - [RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05444) ([github]())\n* RankGAN - [Adversarial Ranking for Language Generation ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.11001) \n* RCGAN - [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02633) \n* ReConNN - [Reconstruction of Simulation-Based Physical Field with Limited Samples by Reconstruction Neural Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00528) \n* Recycle-GAN - [Recycle-GAN: Unsupervised Video Retargeting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05174) \n* RefineGAN - [Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00753) \n* ReGAN - [ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02788) ([github](https:\u002F\u002Fgithub.com\u002FTalkToTheGAN\u002FREGAN))\n* RegCGAN - [Unpaired Multi-Domain Image Generation via Regularized Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02456) \n* RenderGAN - [RenderGAN: Generating Realistic Labeled Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01331) \n* Resembled GAN - [Resembled Generative Adversarial Networks: Two Domains with Similar Attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00947) \n* ResGAN - [Generative Adversarial Network based on Resnet for Conditional Image Restoration](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.04881) \n* RNN-WGAN - [Language Generation with Recurrent Generative Adversarial Networks without Pre-training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01399) ([github](https:\u002F\u002Fgithub.com\u002Famirbar\u002Frnn.wgan))\n* RoCGAN - [Robust Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08657) \n* RPGAN - [Stabilizing GAN Training with Multiple Random Projections](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07831) ([github](https:\u002F\u002Fgithub.com\u002Fayanc\u002Frpgan))\n* RTT-GAN - [Recurrent Topic-Transition GAN for Visual Paragraph Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.07022v2) \n* RWGAN - [Relaxed Wasserstein with Applications to GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07164) \n* SAD-GAN - [SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.08788v1) \n* SAGA - [Generative Adversarial Learning for Spectrum Sensing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00709) \n* SAGAN - [Self-Attention Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08318) \n* SalGAN - [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.01081) ([github](https:\u002F\u002Fgithub.com\u002Fimatge-upc\u002Fsaliency-salgan-2017))\n* SAM - [Sample-Efficient Imitation Learning via Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02064) \n* sAOG - [Deep Structured Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03877) \n* SAR-GAN - [Generating High Quality Visible Images from SAR Images Using CNNs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10036) \n* SBADA-GAN - [From source to target and back: symmetric bi-directional adaptive GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08824) \n* ScarGAN - [ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04500) \n* SCH-GAN - [SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.02488 ) \n* SD-GAN - [Semantically Decomposing the Latent Spaces of Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07904) \n* Sdf-GAN - [Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.06657) \n* SEGAN - [SEGAN: Speech Enhancement Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.09452v1) \n* SeGAN - [SeGAN: Segmenting and Generating the Invisible](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10239) \n* SegAN - [SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01805) \n* Sem-GAN - [Sem-GAN: Semantically-Consistent Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04409) \n* SeqGAN - [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.05473v5) ([github](https:\u002F\u002Fgithub.com\u002FLantaoYu\u002FSeqGAN))\n* SeUDA - [Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00600) \n* SG-GAN - [Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.01726) ([github](https:\u002F\u002Fgithub.com\u002FPeilun-Li\u002FSG-GAN))\n* SG-GAN - [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07509) \n* SGAN - [Texture Synthesis with Spatial Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.08207) \n* SGAN - [Stacked Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.04357v4) ([github](https:\u002F\u002Fgithub.com\u002Fxunhuang1995\u002FSGAN))\n* SGAN - [Steganographic Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.05502) \n* SGAN - [SGAN: An Alternative Training of Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02330) \n* SGAN - [CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.07144) \n* sGAN  - [Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04366) \n* SiftingGAN - [SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04985) \n* SiGAN - [SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08370) \n* SimGAN - [Learning from Simulated and Unsupervised Images through Adversarial Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.07828) \n* SisGAN - [Semantic Image Synthesis via Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.06873) \n* Sketcher-Refiner GAN - [Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08039) \n* SketchGAN - [Adversarial Training For Sketch Retrieval](https:\u002F\u002Farxiv.org\u002Fabs\u002F1607.02748) \n* SketchyGAN - [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.02753) \n* Skip-Thought GAN - [Generating Text through Adversarial Training using Skip-Thought Vectors](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.08703) \n* SL-GAN - [Semi-Latent GAN: Learning to generate and modify facial images from attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.02166) \n* SLSR - [Sparse Label Smoothing for Semi-supervised Person Re-Identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04976) \n* SN-DCGAN - [Generative Adversarial Networks for Unsupervised Object Co-localization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00236) \n* SN-GAN - [Spectral Normalization for Generative Adversarial Networks](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F0B8HZ50DPgR3eSVV6YlF3XzQxSjQ\u002Fview) ([github](https:\u002F\u002Fgithub.com\u002Fpfnet-research\u002Fchainer-gan-lib))\n* SN-PatchGAN - [Free-Form Image Inpainting with Gated Convolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03589) \n* Sobolev GAN - [Sobolev GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04894) \n* Social GAN - [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10892) \n* Softmax GAN - [Softmax GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.06191) \n* SoPhie - [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01482) \n* speech-driven animation GAN - [End-to-End Speech-Driven Facial Animation with Temporal GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09313) \n* Spike-GAN - [Synthesizing realistic neural population activity patterns using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00338) \n* Splitting GAN - [Class-Splitting Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.07359) \n* SR-CNN-VAE-GAN - [Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00509) ([github](https:\u002F\u002Fgithub.com\u002Fmakbari7\u002FSR-CNN-VAE-GAN))\n* SRGAN - [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.04802) \n* SRPGAN - [SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05927) \n* SS-GAN - [Semi-supervised Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.05789) \n* ss-InfoGAN - [Guiding InfoGAN with Semi-Supervision](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.04487) \n* SSGAN - [SSGAN: Secure Steganography Based on Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01613) \n* SSL-GAN - [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06430v1) \n* ST-CGAN - [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02478) \n* ST-GAN - [Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.06762) \n* ST-GAN - [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01837) \n* StackGAN - [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.03242v1) ([github](https:\u002F\u002Fgithub.com\u002Fhanzhanggit\u002FStackGAN))\n* StainGAN - [StainGAN: Stain Style Transfer for Digital Histological Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01601) \n* StarGAN - [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09020) ([github](https:\u002F\u002Fgithub.com\u002Fyunjey\u002FStarGAN))\n* StarGAN-VC - [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02169) \n* SteinGAN - [Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.00797) \n* StepGAN - [Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05599) \n* Super-FAN - [Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02765) \n* SVSGAN - [SVSGAN: Singing Voice Separation via Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.11428) \n* SWGAN - [Solving Approximate Wasserstein GANs to Stationarity](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.08249) \n* SyncGAN - [SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00410) \n* S^2GAN - [Generative Image Modeling using Style and Structure Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.05631v2) \n* T2Net - [T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01454) \n* table-GAN - [Data Synthesis based on Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03384) \n* TAC-GAN - [TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.06412v2) ([github](https:\u002F\u002Fgithub.com\u002Fdashayushman\u002FTAC-GAN))\n* TAN - [Outline Colorization through Tandem Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.08834) \n* tcGAN - [Cross-modal Hallucination for Few-shot Fine-grained Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05147) \n* TD-GAN - [Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07201) \n* tempCycleGAN - [Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03627) \n* tempoGAN - [tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.09710) \n* TequilaGAN - [TequilaGAN: How to easily identify GAN samples](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04919) \n* Text2Shape - [Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08495) \n* textGAN - [Generating Text via Adversarial Training](https:\u002F\u002Fzhegan27.github.io\u002FPapers\u002FtextGAN_nips2016_workshop.pdf) \n* TextureGAN - [TextureGAN: Controlling Deep Image Synthesis with Texture Patches](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02823) \n* TGAN - [Temporal Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06624v1) \n* TGAN - [Tensorizing Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10772) \n* TGAN - [Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02666) \n* TGANs-C - [To Create What You Tell: Generating Videos from Captions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08264) \n* tiny-GAN - [Analysis of Nonautonomous Adversarial Systems](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.05045) \n* TP-GAN - [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04086) \n* TreeGAN - [TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07582) \n* Triple-GAN - [Triple Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.02291v2) \n* tripletGAN - [TripletGAN: Training Generative Model with Triplet Loss](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05084) \n* TV-GAN - [TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02514) \n* Twin-GAN - [Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00946) \n* UGACH - [Unsupervised Generative Adversarial Cross-modal Hashing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00358) \n* UGAN - [Enhancing Underwater Imagery using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.04011) \n* Unim2im - [Unsupervised Image-to-Image Translation with Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.02676) ([github](http:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FUnsup-Im2Im))\n* UNIT - [Unsupervised Image-to-image Translation Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00848) ([github](https:\u002F\u002Fgithub.com\u002Fmingyuliutw\u002FUNIT))\n* Unrolled GAN - [Unrolled Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.02163) ([github](https:\u002F\u002Fgithub.com\u002Fpoolio\u002Funrolled_gan))\n* UT-SCA-GAN - [Spatial Image Steganography Based on Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.07939) \n* UV-GAN - [UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04695) \n* VA-GAN - [Visual Feature Attribution using Wasserstein GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08998) \n* VAC+GAN  - [Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07751) \n* VAE-GAN - [Autoencoding beyond pixels using a learned similarity metric](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.09300) \n* VariGAN - [Multi-View Image Generation from a Single-View](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04886) \n* VAW-GAN - [Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.00849) \n* VEEGAN - [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07761) ([github](https:\u002F\u002Fgithub.com\u002Fakashgit\u002FVEEGAN))\n* VGAN - [Generating Videos with Scene Dynamics](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.02612) ([github](https:\u002F\u002Fgithub.com\u002Fcvondrick\u002Fvideogan))\n* VGAN - [Generative Adversarial Networks as Variational Training of Energy Based Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01799) ([github](https:\u002F\u002Fgithub.com\u002FShuangfei\u002Fvgan))\n* VGAN - [Text Generation Based on Generative Adversarial Nets with Latent Variable](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00170) \n* ViGAN - [Image Generation and Editing with Variational Info Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.04568v1) \n* VIGAN - [VIGAN: Missing View Imputation with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.06724) \n* VoiceGAN - [Voice Impersonation using Generative Adversarial Networks ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06840) \n* VOS-GAN - [VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09092) \n* VRAL - [Variance Regularizing Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.00309) \n* WaterGAN - [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07392v1) \n* WaveGAN - [Synthesizing Audio with Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04208) \n* WaveletGLCA-GAN - [Global and Local Consistent Wavelet-domain Age Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07764) \n* weGAN - [Generative Adversarial Nets for Multiple Text Corpora](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.09127) \n* WGAN - [Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07875v2) ([github](https:\u002F\u002Fgithub.com\u002Fmartinarjovsky\u002FWassersteinGAN))\n* WGAN-CLS - [Text to Image Synthesis Using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00676) \n* WGAN-GP - [Improved Training of Wasserstein GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.00028) ([github](https:\u002F\u002Fgithub.com\u002Figul222\u002Fimproved_wgan_training))\n* WGAN-L1 - [Subsampled Turbulence Removal Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04418) \n* WS-GAN - [Weakly Supervised Generative Adversarial Networks for 3D Reconstruction ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10904) \n* X-GANs - [X-GANs: Image Reconstruction Made Easy for Extreme Cases](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04432) \n* XGAN - [XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05139) \n* ZipNet-GAN - [ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02413) \n* α-GAN - [Variational Approaches for Auto-Encoding Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04987) ([github](https:\u002F\u002Fgithub.com\u002Fvictor-shepardson\u002Falpha-GAN))\n* β-GAN - [Annealed Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07505) \n* Δ-GAN - [Triangle Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.06548) \n\n","# GAN动物园\n\n\u003Cp align=\"center\">\u003Cimg width=\"40%\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhindupuravinash_the-gan-zoo_readme_404db0f30f8c.jpg\" \u002F>\u003C\u002Fp>\n\n每周都有新的 GAN 论文发表，要跟踪所有这些论文实属不易，更不用说研究者们为这些 GAN 起的那些极具创意的名字了！因此，这里列出了一份最初作为一项趣味活动整理的所有已命名 GAN 的清单！\n\n\u003Cp align=\"center\">\u003Cimg width=\"50%\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhindupuravinash_the-gan-zoo_readme_b4ce93351580.jpg\" \u002F>\u003C\u002Fp>\n\n你也可以查看以表格形式呈现的相同数据，并支持按年份筛选或按标题快速搜索 [在此处](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002Fgans.tsv)。\n\n欢迎贡献！请通过 pull request 向 gans.tsv 文件添加链接，格式与现有内容一致；或者创建一个 issue，告诉我有哪些遗漏，或是发起讨论。\n\n不妨看看 [Deep Hunt](https:\u002F\u002Fdeephunt.in)——我的每周 AI 简报，关于这个仓库的 [博客文章](https:\u002F\u002Fmedium.com\u002Fdeep-hunt\u002Fthe-gan-zoo-79597dc8c347)，并关注我在 [Twitter](https:\u002F\u002Fwww.twitter.com\u002Fhindupuravinash) 上的账号。\n\n* 3D-ED-GAN - [Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06375) \n* 3D-GAN - [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.07584) ([github](https:\u002F\u002Fgithub.com\u002Fzck119\u002F3dgan-release))\n* 3D-IWGAN - [Improved Adversarial Systems for 3D Object Generation and Reconstruction](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.09557) ([github](https:\u002F\u002Fgithub.com\u002FEdwardSmith1884\u002F3D-IWGAN))\n* 3D-PhysNet - [3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00328) \n* 3D-RecGAN - [3D Object Reconstruction from a Single Depth View with Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.07969) ([github](https:\u002F\u002Fgithub.com\u002FYang7879\u002F3D-RecGAN))\n* ABC-GAN - [ABC-GAN: Adaptive Blur and Control for improved training stability of Generative Adversarial Networks](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F0B3wEP_lEl0laVTdGcHE2VnRiMlE\u002Fview) ([github](https:\u002F\u002Fgithub.com\u002FIgorSusmelj\u002FABC-GAN))\n* ABC-GAN - [GANs for LIFE: Generative Adversarial Networks for Likelihood Free Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11139) \n* AC-GAN - [Conditional Image Synthesis With Auxiliary Classifier GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.09585) \n* acGAN - [Face Aging With Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.01983) \n* ACGAN - [Coverless Information Hiding Based on Generative adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06951) \n* acGAN - [On-line Adaptative Curriculum Learning for GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00020) \n* ACtuAL - [ACtuAL: Actor-Critic Under Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04755) \n* AdaGAN - [AdaGAN: Boosting Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.02386v1) \n* Adaptive GAN - [Customizing an Adversarial Example Generator with Class-Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.10496) \n* AdvEntuRe - [AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.04680) \n* AdvGAN - [Generating adversarial examples with adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.02610) \n* AE-GAN - [AE-GAN: adversarial eliminating with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05474) \n* AE-OT - [Latent Space Optimal Transport for Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05964) \n* AEGAN - [Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10094) \n* AF-DCGAN - [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization System](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05347) \n* AffGAN - [Amortised MAP Inference for Image Super-resolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.04490) \n* AIM - [Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05972) \n* AL-CGAN - [Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00215) \n* ALI - [Adversarially Learned Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.00704) ([github](https:\u002F\u002Fgithub.com\u002FIshmaelBelghazi\u002FALI))\n* AlignGAN - [AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01400) \n* AlphaGAN - [AlphaGAN: Generative adversarial networks for natural image matting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.10088) \n* AM-GAN - [Activation Maximization Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.02000) \n* AmbientGAN - [AmbientGAN: Generative models from lossy measurements](https:\u002F\u002Fopenreview.net\u002Fforum?id=Hy7fDog0b) ([github](https:\u002F\u002Fgithub.com\u002FAshishBora\u002Fambient-gan))\n* AMC-GAN - [Video Prediction with Appearance and Motion Conditions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.02635) \n* AnoGAN - [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.05921v1) \n* APD - [Adversarial Distillation of Bayesian Neural Network Posteriors](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.10317) \n* APE-GAN - [APE-GAN: Adversarial Perturbation Elimination with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05474) \n* ARAE - [Adversarially Regularized Autoencoders for Generating Discrete Structures](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04223) ([github](https:\u002F\u002Fgithub.com\u002Fjakezhaojb\u002FARAE))\n* ARDA - [Adversarial Representation Learning for Domain Adaptation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01217) \n* ARIGAN - [ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00938) \n* ArtGAN - [ArtGAN: Artwork Synthesis with Conditional Categorial GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.03410) \n* ASDL-GAN - [Automatic Steganographic Distortion Learning Using a Generative Adversarial Network](https:\u002F\u002Fieeexplore.ieee.org\u002Fdocument\u002F8017430\u002F) \n* ATA-GAN - [Attention-Aware Generative Adversarial Networks (ATA-GANs)](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.09070) \n* Attention-GAN - [Attention-GAN for Object Transfiguration in Wild Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.06798) \n* AttGAN - [Arbitrary Facial Attribute Editing: Only Change What You Want](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10678) ([github](https:\u002F\u002Fgithub.com\u002FLynnHo\u002FAttGAN-Tensorflow))\n* AttnGAN - [AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10485) ([github](https:\u002F\u002Fgithub.com\u002Ftaoxugit\u002FAttnGAN))\n* AVID - [AVID: Adversarial Visual Irregularity Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09521) \n* B-DCGAN - [B-DCGAN:Evaluation of Binarized DCGAN for FPGA](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10930) \n* b-GAN - [Generative Adversarial Nets from a Density Ratio Estimation Perspective](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.02920) \n* BAGAN - [BAGAN: Data Augmentation with Balancing GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09655) \n* Bayesian GAN - [Deep and Hierarchical Implicit Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08896) \n* Bayesian GAN - [Bayesian GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09558) ([github](https:\u002F\u002Fgithub.com\u002Fandrewgordonwilson\u002Fbayesgan\u002F))\n* BCGAN - [Bayesian Conditional Generative Adverserial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05477) \n* BCGAN - [Bidirectional Conditional Generative Adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.07461) \n* BEAM - [Boltzmann Encoded Adversarial Machines](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08682) \n* BEGAN - [BEGAN: Boundary Equilibrium Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10717) \n* BEGAN-CS - [Escaping from Collapsing Modes in a Constrained Space](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07258) \n* Bellman GAN - [Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01960) \n* BGAN - [Binary Generative Adversarial Networks for Image Retrieval](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.04150) ([github](https:\u002F\u002Fgithub.com\u002Fhtconquer\u002FBGAN))\n* Bi-GAN - [Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.10244) \n* BicycleGAN - [Toward Multimodal Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11586) ([github](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FBicycleGAN))\n* BiGAN - [Adversarial Feature Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.09782v7) \n* BinGAN - [BinGAN: Learning Compact Binary Descriptors with a Regularized GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06778) \n* BourGAN - [BourGAN: Generative Networks with Metric Embeddings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07674) \n* BranchGAN - [Branched Generative Adversarial Networks for Multi-Scale Image Manifold Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08467) \n* BRE - [Improving GAN Training via Binarized Representation Entropy (BRE) Regularization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.03644) ([github](https:\u002F\u002Fgithub.com\u002FBorealisAI\u002Fbre-gan))\n* BridgeGAN - [Generative Adversarial Frontal View to Bird View Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00327) \n* BS-GAN - [Boundary-Seeking Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08431v1) \n* BubGAN - [BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02266) \n* BWGAN - [Banach Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06621) \n* C-GAN  - [Face Aging with Contextual Generative Adversarial Nets ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.00237 ) \n* C-RNN-GAN - [C-RNN-GAN: Continuous recurrent neural networks with adversarial training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.09904) ([github](https:\u002F\u002Fgithub.com\u002Folofmogren\u002Fc-rnn-gan\u002F))\n* CA-GAN - [Composition-aided Sketch-realistic Portrait Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00899) \n* CaloGAN - [CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02355) ([github](https:\u002F\u002Fgithub.com\u002Fhep-lbdl\u002FCaloGAN))\n* CAN - [CAN: Creative Adversarial Networks, Generating Art by Learning About Styles and Deviating from Style Norms](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.07068) \n* CapsGAN - [CapsGAN: Using Dynamic Routing for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03968) \n* CapsuleGAN - [CapsuleGAN: Generative Adversarial Capsule Network ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06167) \n* CatGAN - [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06390v2) \n* CatGAN - [CatGAN: Coupled Adversarial Transfer for Domain Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08904) \n* CausalGAN - [CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.02023) \n* CC-GAN - [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06430) ([github](https:\u002F\u002Fgithub.com\u002Fedenton\u002Fcc-gan))\n* cd-GAN - [Conditional Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00251) \n* CDcGAN - [Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.09105) \n* CE-GAN - [Deep Learning for Imbalance Data Classification using Class Expert Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04585) \n* CFG-GAN - [Composite Functional Gradient Learning of Generative Adversarial Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.06309) \n* CGAN - [Conditional Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1411.1784) \n* CGAN - [Controllable Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00598) \n* Chekhov GAN - [An Online Learning Approach to Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.03269) \n* ciGAN - [Conditional Infilling GANs for Data Augmentation in Mammogram Classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08093) \n* CinCGAN - [Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00437) \n* CipherGAN - [Unsupervised Cipher Cracking Using Discrete GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.04883) \n* ClusterGAN - [ClusterGAN : Latent Space Clustering in Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.03627) \n* CM-GAN - [CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.05106) \n* CoAtt-GAN - [Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.07613) \n* CoGAN - [Coupled Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.07536v2) \n* ComboGAN - [ComboGAN: Unrestrained Scalability for Image Domain Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.06909) ([github](https:\u002F\u002Fgithub.com\u002FAAnoosheh\u002FComboGAN))\n* ConceptGAN - [Learning Compositional Visual Concepts with Mutual Consistency](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06148) \n* Conditional cycleGAN - [Conditional CycleGAN for Attribute Guided Face Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09966) \n* constrast-GAN - [Generative Semantic Manipulation with Contrasting GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00315) \n* Context-RNN-GAN - [Contextual RNN-GANs for Abstract Reasoning Diagram Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.09444) \n* CorrGAN - [Correlated discrete data generation using adversarial training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00925) \n* Coulomb GAN - [Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.08819) \n* Cover-GAN - [Generative Steganography with Kerckhoffs' Principle based on Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04916) \n* cowboy - [Defending Against Adversarial Attacks by Leveraging an Entire GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10652) \n* CR-GAN - [CR-GAN: Learning Complete Representations for Multi-view Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.11191) \n* Cramèr GAN  - [The Cramer Distance as a Solution to Biased Wasserstein Gradients](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10743) \n* Cross-GAN - [Crossing Generative Adversarial Networks for Cross-View Person Re-identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.01760) \n* crVAE-GAN - [Channel-Recurrent Variational Autoencoders](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.03729) \n* CS-GAN - [Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.04887) \n* CSG - [Speech-Driven Expressive Talking Lips with Conditional Sequential Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00154) \n* CT-GAN - [CT-GAN: Conditional Transformation Generative Adversarial Network for Image Attribute Modification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04812) \n* CVAE-GAN - [CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10155) \n* CycleGAN - [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10593) ([github](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FCycleGAN))\n* D-GAN - [Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10267) \n* D-WCGAN - [I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00290) \n* D2GAN - [Dual Discriminator Generative Adversarial Nets](http:\u002F\u002Farxiv.org\u002Fabs\u002F1709.03831) \n* D2IA-GAN - [Tagging like Humans: Diverse and Distinct Image Annotation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00113) \n* DA-GAN  - [DA-GAN: Instance-level Image Translation by Deep Attention Generative Adversarial Networks (with Supplementary Materials)](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06454) \n* DADA - [DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00981) \n* DAGAN - [Data Augmentation Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04340) \n* DAN - [Distributional Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09549) \n* DBLRGAN - [Adversarial Spatio-Temporal Learning for Video Deblurring](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00533) \n* DCGAN - [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06434) ([github](https:\u002F\u002Fgithub.com\u002FNewmu\u002Fdcgan_code))\n* DE-GAN - [Generative Adversarial Networks with Decoder-Encoder Output Noise](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03923) \n* DeblurGAN - [DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.07064) ([github](https:\u002F\u002Fgithub.com\u002FKupynOrest\u002FDeblurGAN))\n* DeepFD - [Learning to Detect Fake Face Images in the Wild](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.08754) \n* Defense-GAN - [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.06605 ) ([github](https:\u002F\u002Fgithub.com\u002Fkabkabm\u002Fdefensegan))\n* Defo-Net - [Defo-Net: Learning Body Deformation using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05928) \n* DeliGAN - [DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02071) ([github](https:\u002F\u002Fgithub.com\u002Fval-iisc\u002Fdeligan))\n* DF-GAN - [Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04646) \n* DialogWAE - [DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.12352) \n* DiscoGAN - [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.05192v1) \n* DistanceGAN - [One-Sided Unsupervised Domain Mapping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00826) \n* DM-GAN - [Dual Motion GAN for Future-Flow Embedded Video Prediction](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.00284) \n* DMGAN - [Disconnected Manifold Learning for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00880) \n* DNA-GAN - [DNA-GAN: Learning Disentangled Representations from Multi-Attribute Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05415) \n* DOPING - [DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07632) \n* dp-GAN - [Differentially Private Releasing via Deep Generative Model](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.01594) \n* DP-GAN - [DP-GAN: Diversity-Promoting Generative Adversarial Network for Generating Informative and Diversified Text ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.01345 ) \n* DPGAN  - [Differentially Private Generative Adversarial Network ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06739) \n* DR-GAN - [Representation Learning by Rotating Your Faces](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.11136) \n* DRAGAN - [How to Train Your DRAGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07215) ([github](https:\u002F\u002Fgithub.com\u002Fkodalinaveen3\u002FDRAGAN))\n* Dropout-GAN - [Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.11346) \n* DRPAN - [Discriminative Region Proposal Adversarial Networks for High-Quality Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09554) \n* DSH-GAN - [Deep Semantic Hashing with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08275) \n* DSP-GAN - [Depth Structure Preserving Scene Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00212) \n* DTLC-GAN - [Generative Adversarial Image Synthesis with Decision Tree Latent Controller](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10603) \n* DTN - [Unsupervised Cross-Domain Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.02200) \n* DTR-GAN - [DTR-GAN: Dilated Temporal Relational Adversarial Network for Video Summarization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.11228) \n* DualGAN - [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.02510v1) \n* Dualing GAN - [Dualing GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.06216) \n* DVGAN - [Human Motion Modeling using DVGANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.10652) \n* Dynamics Transfer GAN - [Dynamics Transfer GAN: Generating Video by Transferring Arbitrary Temporal Dynamics from a Source Video to a Single Target Image](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.03534) \n* E-GAN - [Evolutionary Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00657) \n* EAR - [Generative Model for Heterogeneous Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.09858) \n* EBGAN - [Energy-based Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.03126v4) \n* ecGAN - [eCommerceGAN : A Generative Adversarial Network for E-commerce](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.03244) \n* ED\u002F\u002FGAN - [Stabilizing Training of Generative Adversarial Networks through Regularization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09367) \n* Editable GAN - [Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.07700) \n* EGAN - [Enhanced Experience Replay Generation for Efficient Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08245) \n* EL-GAN - [EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05525) \n* ELEGANT - [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10562) \n* EnergyWGAN - [Energy-relaxed Wassertein GANs (EnergyWGAN): Towards More Stable and High Resolution Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01026) \n* ESRGAN - [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00219) \n* ExGAN - [Eye In-Painting with Exemplar Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.03999) \n* ExposureGAN - [Exposure: A White-Box Photo Post-Processing Framework](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.09602) ([github](https:\u002F\u002Fgithub.com\u002Fyuanming-hu\u002Fexposure))\n* ExprGAN - [ExprGAN: Facial Expression Editing with Controllable Expression Intensity](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.03842) \n* f-CLSWGAN - [Feature Generating Networks for Zero-Shot Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00981) \n* f-GAN - [f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.00709) \n* FairGAN - [FairGAN: Fairness-aware Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11202) \n* Fairness GAN - [Fairness GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09910) \n* FakeGAN - [Detecting Deceptive Reviews using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10364) \n* FBGAN - [Feedback GAN (FBGAN) for DNA: a Novel Feedback-Loop Architecture for Optimizing Protein Functions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01694) \n* FBGAN - [Featurized Bidirectional GAN: Adversarial Defense via Adversarially Learned Semantic Inference](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07862) \n* FC-GAN - [Fast-converging Conditional Generative Adversarial Networks for Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.01972) \n* FF-GAN - [Towards Large-Pose Face Frontalization in the Wild](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.06244) \n* FGGAN - [Adversarial Learning for Fine-grained Image Search](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.02247) \n* Fictitious GAN - [Fictitious GAN: Training GANs with Historical Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08647) \n* FIGAN - [Frame Interpolation with Multi-Scale Deep Loss Functions and Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06045) \n* Fila-GAN - [Synthesizing Filamentary Structured Images with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02185) \n* First Order GAN  - [First Order Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04591) ([github](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffirst_order_gan))\n* Fisher GAN - [Fisher GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09675) \n* Flow-GAN - [Flow-GAN: Bridging implicit and prescribed learning in generative models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08868) \n* FrankenGAN - [rankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07179) \n* FSEGAN - [Exploring Speech Enhancement with Generative Adversarial Networks for Robust Speech Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05747) \n* FTGAN - [Hierarchical Video Generation from Orthogonal Information: Optical Flow and Texture](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09618) \n* FusedGAN - [Semi-supervised FusedGAN for Conditional Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.05551) \n* FusionGAN - [Learning to Fuse Music Genres with Generative Adversarial Dual Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01456) \n* FusionGAN - [Generating a Fusion Image: One's Identity and Another's Shape](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.07455) \n* G2-GAN - [Geometry Guided Adversarial Facial Expression Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.03474) \n* GAAN - [Generative Adversarial Autoencoder Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08887) \n* GAF - [Generative Adversarial Forests for Better Conditioned Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.05185) \n* GAGAN - [GAGAN: Geometry-Aware Generative Adverserial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00684) \n* GAIA - [Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.06650) \n* GAIN  - [GAIN: Missing Data Imputation using Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02920) \n* GAMN - [Generative Adversarial Mapping Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.09820) \n* GAN - [Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1406.2661) ([github](https:\u002F\u002Fgithub.com\u002Fgoodfeli\u002Fadversarial))\n* GAN Lab - [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.01587) \n* GAN Q-learning - [GAN Q-learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.04874) \n* GAN-AD - [Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04758) \n* GAN-ATV - [A Novel Approach to Artistic Textual Visualization via GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10553) \n* GAN-CLS - [Generative Adversarial Text to Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.05396) ([github](https:\u002F\u002Fgithub.com\u002Freedscot\u002Ficml2016))\n* GAN-RS - [Towards Qualitative Advancement of Underwater Machine Vision with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00736) \n* GAN-SD - [Virtual-Taobao: Virtualizing Real-world Online Retail Environment for Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.10000) \n* GAN-sep - [GANs for Biological Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.04692) ([github](https:\u002F\u002Fgithub.com\u002Faosokin\u002Fbiogans))\n* GAN-VFS - [Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02681) \n* GAN-Word2Vec - [Adversarial Training of Word2Vec for Basket Completion](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08720) \n* GANAX - [GANAX: A Unified MIMD-SIMD Acceleration for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01107) \n* GANCS - [Deep Generative Adversarial Networks for Compressed Sensing Automates MRI](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00051) \n* GANDI - [Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.01391) \n* GANG - [GANGs: Generative Adversarial Network Games](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00679) \n* GANG - [Beyond Local Nash Equilibria for Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07268) \n* GANosaic - [GANosaic: Mosaic Creation with Generative Texture Manifolds](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00269) \n* GANVO - [GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05786) \n* GAP - [Context-Aware Generative Adversarial Privacy](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09549) \n* GAP - [Generative Adversarial Privacy](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.05306) \n* GATS - [Sample-Efficient Deep RL with Generative Adversarial Tree Search](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05780) \n* GAWWN - [Learning What and Where to Draw](https:\u002F\u002Farxiv.org\u002Fabs\u002F1610.02454) ([github](https:\u002F\u002Fgithub.com\u002Freedscot\u002Fnips2016))\n* GC-GAN - [Geometry-Contrastive Generative Adversarial Network for Facial Expression Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.01822 ) \n* GcGAN - [Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05852) \n* GeneGAN - [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.04932) ([github](https:\u002F\u002Fgithub.com\u002FPrinsphield\u002FGeneGAN))\n* GeoGAN - [Generating Instance Segmentation Annotation by Geometry-guided GAN ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.08839 ) \n* Geometric GAN - [Geometric GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.02894) \n* GIN - [Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04495) \n* GLCA-GAN - [Global and Local Consistent Age Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.08390) \n* GM-GAN - [Gaussian Mixture Generative Adversarial Networks for Diverse Datasets, and the Unsupervised Clustering of Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.10356) \n* GMAN - [Generative Multi-Adversarial Networks](http:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01673) \n* GMM-GAN - [Towards Understanding the Dynamics of Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09884) \n* GoGAN - [Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04865) \n* GONet - [GONet: A Semi-Supervised Deep Learning Approach For Traversability Estimation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.03254) \n* GP-GAN - [GP-GAN: Towards Realistic High-Resolution Image Blending](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.07195) ([github](https:\u002F\u002Fgithub.com\u002Fwuhuikai\u002FGP-GAN))\n* GP-GAN - [GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.00962) \n* GPU - [A generative adversarial framework for positive-unlabeled classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08054) \n* GRAN - [Generating images with recurrent adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1602.05110) ([github](https:\u002F\u002Fgithub.com\u002Fjiwoongim\u002FGRAN))\n* Graphical-GAN - [Graphical Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03429) \n* GraphSGAN - [Semi-supervised Learning on Graphs with Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00130) \n* GraspGAN - [Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.07857) \n* GT-GAN - [Deep Graph Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09980) \n* HAN - [Chinese Typeface Transformation with Hierarchical Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06448) \n* HAN - [Bidirectional Learning for Robust Neural Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08006) \n* HiGAN - [Exploiting Images for Video Recognition with Hierarchical Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.04384) \n* HP-GAN - [HP-GAN: Probabilistic 3D human motion prediction via GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09561) \n* HR-DCGAN - [High-Resolution Deep Convolutional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06491) \n* hredGAN - [Multi-turn Dialogue Response Generation in an Adversarial Learning framework](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11752) \n* IAN - [Neural Photo Editing with Introspective Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.07093) ([github](https:\u002F\u002Fgithub.com\u002Fajbrock\u002FNeural-Photo-Editor))\n* IcGAN - [Invertible Conditional GANs for image editing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06355) ([github](https:\u002F\u002Fgithub.com\u002FGuim3\u002FIcGAN))\n* ID-CGAN - [Image De-raining Using a Conditional Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.05957v3) \n* IdCycleGAN - [Face Translation between Images and Videos using Identity-aware CycleGAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00971) \n* IFcVAEGAN - [Conditional Autoencoders with Adversarial Information Factorization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05175) \n* iGAN - [Generative Visual Manipulation on the Natural Image Manifold](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.03552v2) ([github](https:\u002F\u002Fgithub.com\u002Fjunyanz\u002FiGAN))\n* IGMM-GAN - [Coupled IGMM-GANs for deep multimodal anomaly detection in human mobility data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02728) \n* Improved GAN - [Improved Techniques for Training GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.03498) ([github](https:\u002F\u002Fgithub.com\u002Fopenai\u002Fimproved-gan))\n* In2I - [In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09334) \n* InfoGAN - [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1606.03657v1) ([github](https:\u002F\u002Fgithub.com\u002Fopenai\u002FInfoGAN))\n* IntroVAE - [IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.06358) \n* IR2VI - [IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.09565) \n* IRGAN - [IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10513v1) \n* IRGAN - [Generative Adversarial Nets for Information Retrieval: Fundamentals and Advances](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03577) \n* ISGAN - [Invisible Steganography via Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08571) \n* ISP-GPM - [Inner Space Preserving Generative Pose Machine](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.02104) \n* Iterative-GAN - [Two Birds with One Stone: Iteratively Learn Facial Attributes with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06078) ([github](https:\u002F\u002Fgithub.com\u002Fpunkcure\u002FIterative-GAN))\n* IterGAN - [IterGANs: Iterative GANs to Learn and Control 3D Object Transformation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.05651) \n* IVE-GAN - [IVE-GAN: Invariant Encoding Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08646) \n* iVGAN - [Towards an Understanding of Our World by GANing Videos in the Wild](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11453) ([github](https:\u002F\u002Fgithub.com\u002Fbernhard2202\u002Fimproved-video-gan))\n* IWGAN - [On Unifying Deep Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00550) \n* JointGAN - [JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02978) \n* JR-GAN - [JR-GAN: Jacobian Regularization for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.09235) \n* KBGAN - [KBGAN: Adversarial Learning for Knowledge Graph Embeddings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04071) \n* KGAN - [KGAN: How to Break The Minimax Game in GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.01744) \n* l-GAN - [Representation Learning and Adversarial Generation of 3D Point Clouds](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.02392) \n* LAC-GAN - [Grounded Language Understanding for Manipulation Instructions Using GAN-Based Classification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.05096) \n* LAGAN - [Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.05927) \n* LAPGAN - [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1506.05751) ([github](https:\u002F\u002Fgithub.com\u002Ffacebook\u002Feyescream))\n* LB-GAN - [Load Balanced GANs for Multi-view Face Image Synthesis](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.07447) \n* LBT - [Learning Implicit Generative Models by Teaching Explicit Ones](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03870) \n* LCC-GAN - [Adversarial Learning with Local Coordinate Coding](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.04895) \n* LD-GAN - [Linear Discriminant Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.07831) \n* LDAN - [Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.01993) \n* LeakGAN - [Long Text Generation via Adversarial Training with Leaked Information](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.08624) \n* LeGAN - [Likelihood Estimation for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.07530) \n* LGAN - [Global versus Localized Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06020) \n* Lipizzaner - [Towards Distributed Coevolutionary GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08194) \n* LR-GAN - [LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.01560v1) \n* LS-GAN - [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.06264) \n* LSGAN - [Least Squares Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.04076v3) \n* M-AAE - [Mask-aware Photorealistic Face Attribute Manipulation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08882) \n* MAD-GAN - [Multi-Agent Diverse Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.02906) \n* MAGAN - [MAGAN: Margin Adaptation for Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.03817v1) \n* MAGAN - [MAGAN: Aligning Biological Manifolds](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00385) \n* MalGAN - [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.05983v1) \n* MaliGAN - [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07983) \n* manifold-WGAN - [Manifold-valued Image Generation with Wasserstein Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01551) \n* MARTA-GAN - [Deep Unsupervised Representation Learning for Remote Sensing Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.08879) \n* MaskGAN - [MaskGAN: Better Text Generation via Filling in the ______ ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.07736 ) \n* MC-GAN - [Multi-Content GAN for Few-Shot Font Style Transfer](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00516) ([github](https:\u002F\u002Fgithub.com\u002Fazadis\u002FMC-GAN))\n* MC-GAN - [MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.01123) \n* McGAN - [McGan: Mean and Covariance Feature Matching GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.08398v1) \n* MD-GAN - [Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.07592) \n* MDGAN - [Mode Regularized Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.02136) \n* MedGAN - [Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.06490v1) \n* MedGAN - [MedGAN: Medical Image Translation using GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.06397) \n* MEGAN - [MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02481) \n* MelanoGAN - [MelanoGANs: High Resolution Skin Lesion Synthesis with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04338) \n* memoryGAN - [Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01500) \n* MeRGAN - [Memory Replay GANs: learning to generate images from new categories without forgetting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02058) \n* MGAN - [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1604.04382) ([github](https:\u002F\u002Fgithub.com\u002Fchuanli11\u002FMGANs))\n* MGGAN - [Multi-Generator Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.02556) \n* MGGAN - [MGGAN: Solving Mode Collapse using Manifold Guided Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04391) \n* MIL-GAN - [Multimodal Storytelling via Generative Adversarial Imitation Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.01455) \n* MinLGAN - [Anomaly Detection via Minimum Likelihood Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.00200) \n* MIX+GAN - [Generalization and Equilibrium in Generative Adversarial Nets (GANs)](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00573v3) \n* MIXGAN - [MIXGAN: Learning Concepts from Different Domains for Mixture Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.01659) \n* MLGAN - [Metric Learning-based Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02792) \n* MMC-GAN - [A Multimodal Classifier Generative Adversarial Network for Carry and Place Tasks from Ambiguous Language Instructions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03847) \n* MMD-GAN - [MMD GAN: Towards Deeper Understanding of Moment Matching Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08584) ([github](https:\u002F\u002Fgithub.com\u002Fdougalsutherland\u002Fopt-mmd))\n* MMGAN - [MMGAN: Manifold Matching Generative Adversarial Network for Generating Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08273) \n* MoCoGAN - [MoCoGAN: Decomposing Motion and Content for Video Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.04993) ([github](https:\u002F\u002Fgithub.com\u002Fsergeytulyakov\u002Fmocogan))\n* Modified GAN-CLS - [Generate the corresponding Image from Text Description using Modified GAN-CLS Algorithm](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.11302) \n* ModularGAN - [Modular Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03343) \n* MolGAN - [MolGAN: An implicit generative model for small molecular graphs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.11973) \n* MPM-GAN - [Message Passing Multi-Agent GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.01294) \n* MS-GAN - [Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks](http:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F7014-temporal-coherency-based-criteria-for-predicting-video-frames-using-deep-multi-stage-generative-adversarial-networks) \n* MTGAN - [MTGAN: Speaker Verification through Multitasking Triplet Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09059) \n* MuseGAN - [MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.06298) \n* MV-BiGAN - [Multi-view Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.02019v1) \n* N2RPP - [N2RPP: An Adversarial Network to Rebuild Plantar Pressure for ACLD Patients](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02825) \n* NAN - [Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.03287) \n* NCE-GAN - [Dihedral angle prediction using generative adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10996) \n* ND-GAN - [Novelty Detection with GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10560) \n* NetGAN - [NetGAN: Generating Graphs via Random Walks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00816) \n* OCAN - [One-Class Adversarial Nets for Fraud Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01798) \n* OptionGAN - [OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.06683) \n* ORGAN - [Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10843) \n* ORGAN - [3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversary Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.06363) \n* OT-GAN - [Improving GANs Using Optimal Transport](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.05573) \n* PacGAN - [PacGAN: The power of two samples in generative adversarial networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04086) \n* PAN - [Perceptual Adversarial Networks for Image-to-Image Transformation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09138) \n* PassGAN - [PassGAN: A Deep Learning Approach for Password Guessing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00440) \n* PD-WGAN - [Primal-Dual Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09575) \n* Perceptual GAN - [Perceptual Generative Adversarial Networks for Small Object Detection](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05274) \n* PGAN - [Probabilistic Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01886) \n* PGD-GAN - [Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.08406) \n* PGGAN - [Patch-Based Image Inpainting with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.07422) \n* PIONEER - [Pioneer Networks: Progressively Growing Generative Autoencoder](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03026) \n* Pip-GAN - [Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.10742) \n* pix2pix - [Image-to-Image Translation with Conditional Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.07004) ([github](https:\u002F\u002Fgithub.com\u002Fphillipi\u002Fpix2pix))\n* pix2pixHD - [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.11585) ([github](https:\u002F\u002Fgithub.com\u002FNVIDIA\u002Fpix2pixHD))\n* PixelGAN - [PixelGAN Autoencoders](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.00531) \n* PM-GAN - [PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.06248) \n* PN-GAN - [Pose-Normalized Image Generation for Person Re-identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02225) \n* POGAN - [Perceptually Optimized Generative Adversarial Network for Single Image Dehazing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.01084) \n* Pose-GAN - [The Pose Knows: Video Forecasting by Generating Pose Futures](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.00053) \n* PP-GAN - [Privacy-Protective-GAN for Face De-identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.08906) \n* PPAN - [Privacy-Preserving Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.07008) \n* PPGN - [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.00005) \n* PrGAN - [3D Shape Induction from 2D Views of Multiple Objects](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.05872) \n* ProGanSR - [A Fully Progressive Approach to Single-Image Super-Resolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02900) \n* Progressive GAN - [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10196) ([github](https:\u002F\u002Fgithub.com\u002Ftkarras\u002Fprogressive_growing_of_gans))\n* PS-GAN - [Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.02047) \n* PSGAN - [Learning Texture Manifolds with the Periodic Spatial GAN](http:\u002F\u002Farxiv.org\u002Fabs\u002F1705.06566) \n* PSGAN - [PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.03371) \n* PS²-GAN - [High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10182) \n* RadialGAN - [RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06403) \n* RaGAN - [The relativistic discriminator: a key element missing from standard GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00734) \n* RAN - [RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05444) ([github]())\n* RankGAN - [Adversarial Ranking for Language Generation ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.11001) \n* RCGAN - [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02633) \n* ReConNN - [Reconstruction of Simulation-Based Physical Field with Limited Samples by Reconstruction Neural Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00528) \n* Recycle-GAN - [Recycle-GAN: Unsupervised Video Retargeting](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05174) \n* RefineGAN - [Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.00753) \n* ReGAN - [ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02788) ([github](https:\u002F\u002Fgithub.com\u002FTalkToTheGAN\u002FREGAN))\n* RegCGAN - [Unpaired Multi-Domain Image Generation via Regularized Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.02456) \n* RenderGAN - [RenderGAN: Generating Realistic Labeled Data](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01331) \n* Resembled GAN - [Resembled Generative Adversarial Networks: Two Domains with Similar Attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00947) \n* ResGAN - [Generative Adversarial Network based on Resnet for Conditional Image Restoration](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.04881) \n* RNN-WGAN - [Language Generation with Recurrent Generative Adversarial Networks without Pre-training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01399) ([github](https:\u002F\u002Fgithub.com\u002Famirbar\u002Frnn.wgan))\n* RoCGAN - [Robust Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08657) \n* RPGAN - [Stabilizing GAN Training with Multiple Random Projections](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07831) ([github](https:\u002F\u002Fgithub.com\u002Fayanc\u002Frpgan))\n* RTT-GAN - [Recurrent Topic-Transition GAN for Visual Paragraph Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.07022v2) \n* RWGAN - [Relaxed Wasserstein with Applications to GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07164) \n* SAD-GAN - [SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.08788v1) \n* SAGA - [Generative Adversarial Learning for Spectrum Sensing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00709) \n* SAGAN - [Self-Attention Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.08318) \n* SalGAN - [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.01081) ([github](https:\u002F\u002Fgithub.com\u002Fimatge-upc\u002Fsaliency-salgan-2017))\n* SAM - [Sample-Efficient Imitation Learning via Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.02064) \n* sAOG - [Deep Structured Generative Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.03877) \n* SAR-GAN - [Generating High Quality Visible Images from SAR Images Using CNNs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.10036) \n* SBADA-GAN - [From source to target and back: symmetric bi-directional adaptive GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.08824) \n* ScarGAN - [ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04500) \n* SCH-GAN - [SCH-GAN: Semi-supervised Cross-modal Hashing by Generative Adversarial Network ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.02488 ) \n* SD-GAN - [Semantically Decomposing the Latent Spaces of Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07904) \n* Sdf-GAN - [Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.06657) \n* SEGAN - [SEGAN: Speech Enhancement Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.09452v1) \n* SeGAN - [SeGAN: Segmenting and Generating the Invisible](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.10239) \n* SegAN - [SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.01805) \n* Sem-GAN - [Sem-GAN: Semantically-Consistent Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04409) \n* SeqGAN - [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.05473v5) ([github](https:\u002F\u002Fgithub.com\u002FLantaoYu\u002FSeqGAN))\n* SeUDA - [Semantic-Aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00600) \n* SG-GAN - [Semantic-aware Grad-GAN for Virtual-to-Real Urban Scene Adaption](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.01726) ([github](https:\u002F\u002Fgithub.com\u002FPeilun-Li\u002FSG-GAN))\n* SG-GAN - [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.07509) \n* SGAN - [Texture Synthesis with Spatial Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.08207) \n* SGAN - [Stacked Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.04357v4) ([github](https:\u002F\u002Fgithub.com\u002Fxunhuang1995\u002FSGAN))\n* SGAN - [Steganographic Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.05502) \n* SGAN - [SGAN: An Alternative Training of Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02330) \n* SGAN - [CT Image Enhancement Using Stacked Generative Adversarial Networks and Transfer Learning for Lesion Segmentation Improvement](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.07144) \n* sGAN  - [Generative Adversarial Training for MRA Image Synthesis Using Multi-Contrast MRI](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.04366) \n* SiftingGAN - [SiftingGAN: Generating and Sifting Labeled Samples to Improve the Remote Sensing Image Scene Classification Baseline in vitro](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04985) \n* SiGAN - [SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.08370) \n* SimGAN - [Learning from Simulated and Unsupervised Images through Adversarial Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.07828) \n* SisGAN - [Semantic Image Synthesis via Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.06873) \n* Sketcher-Refiner GAN - [Learning Myelin Content in Multiple Sclerosis from Multimodal MRI through Adversarial Training](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08039) \n* SketchGAN - [Adversarial Training For Sketch Retrieval](https:\u002F\u002Farxiv.org\u002Fabs\u002F1607.02748) \n* SketchyGAN - [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.02753) \n* Skip-Thought GAN - [Generating Text through Adversarial Training using Skip-Thought Vectors](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.08703) \n* SL-GAN - [Semi-Latent GAN: Learning to generate and modify facial images from attributes](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.02166) \n* SLSR - [Sparse Label Smoothing for Semi-supervised Person Re-Identification](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.04976) \n* SN-DCGAN - [Generative Adversarial Networks for Unsupervised Object Co-localization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00236) \n* SN-GAN - [Spectral Normalization for Generative Adversarial Networks](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F0B8HZ50DPgR3eSVV6YlF3XzQxSjQ\u002Fview) ([github](https:\u002F\u002Fgithub.com\u002Fpfnet-research\u002Fchainer-gan-lib))\n* SN-PatchGAN - [Free-Form Image Inpainting with Gated Convolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03589) \n* Sobolev GAN - [Sobolev GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.04894) \n* Social GAN - [Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.10892) \n* Softmax GAN - [Softmax GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.06191) \n* SoPhie - [SoPhie: An Attentive GAN for Predicting Paths Compliant to Social and Physical Constraints](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.01482) \n* speech-driven animation GAN - [End-to-End Speech-Driven Facial Animation with Temporal GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.09313) \n* Spike-GAN - [Synthesizing realistic neural population activity patterns using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.00338) \n* Splitting GAN - [Class-Splitting Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.07359) \n* SR-CNN-VAE-GAN - [Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.00509) ([github](https:\u002F\u002Fgithub.com\u002Fmakbari7\u002FSR-CNN-VAE-GAN))\n* SRGAN - [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.04802) \n* SRPGAN - [SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.05927) \n* SS-GAN - [Semi-supervised Conditional GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.05789) \n* ss-InfoGAN - [Guiding InfoGAN with Semi-Supervision](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.04487) \n* SSGAN - [SSGAN: Secure Steganography Based on Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.01613) \n* SSL-GAN - [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06430v1) \n* ST-CGAN - [Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02478) \n* ST-GAN - [Style Transfer Generative Adversarial Networks: Learning to Play Chess Differently](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.06762) \n* ST-GAN - [ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.01837) \n* StackGAN - [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.03242v1) ([github](https:\u002F\u002Fgithub.com\u002Fhanzhanggit\u002FStackGAN))\n* StainGAN - [StainGAN: Stain Style Transfer for Digital Histological Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.01601) \n* StarGAN - [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.09020) ([github](https:\u002F\u002Fgithub.com\u002Fyunjey\u002FStarGAN))\n* StarGAN-VC - [StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.02169) \n* SteinGAN - [Learning Deep Energy Models: Contrastive Divergence vs. Amortized MLE](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.00797) \n* StepGAN - [Improving Conditional Sequence Generative Adversarial Networks by Stepwise Evaluation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.05599) \n* Super-FAN - [Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02765) \n* SVSGAN - [SVSGAN: Singing Voice Separation via Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.11428) \n* SWGAN - [Solving Approximate Wasserstein GANs to Stationarity](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.08249) \n* SyncGAN - [SyncGAN: Synchronize the Latent Space of Cross-modal Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00410) \n* S^2GAN - [Generative Image Modeling using Style and Structure Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1603.05631v2) \n* T2Net - [T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.01454) \n* table-GAN - [Data Synthesis based on Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03384) \n* TAC-GAN - [TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.06412v2) ([github](https:\u002F\u002Fgithub.com\u002Fdashayushman\u002FTAC-GAN))\n* TAN - [Outline Colorization through Tandem Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.08834) \n* tcGAN - [Cross-modal Hallucination for Few-shot Fine-grained Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.05147) \n* TD-GAN - [Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07201) \n* tempCycleGAN - [Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.03627) \n* tempoGAN - [tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.09710) \n* TequilaGAN - [TequilaGAN: How to easily identify GAN samples](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04919) \n* Text2Shape - [Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.08495) \n* textGAN - [Generating Text via Adversarial Training](https:\u002F\u002Fzhegan27.github.io\u002FPapers\u002FtextGAN_nips2016_workshop.pdf) \n* TextureGAN - [TextureGAN: Controlling Deep Image Synthesis with Texture Patches](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.02823) \n* TGAN - [Temporal Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.06624v1) \n* TGAN - [Tensorizing Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.10772) \n* TGAN - [Tensor-Generative Adversarial Network with Two-dimensional Sparse Coding: Application to Real-time Indoor Localization](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02666) \n* TGANs-C - [To Create What You Tell: Generating Videos from Captions](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.08264) \n* tiny-GAN - [Analysis of Nonautonomous Adversarial Systems](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.05045) \n* TP-GAN - [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04086) \n* TreeGAN - [TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.07582) \n* Triple-GAN - [Triple Generative Adversarial Nets](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.02291v2) \n* tripletGAN - [TripletGAN: Training Generative Model with Triplet Loss](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05084) \n* TV-GAN - [TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.02514) \n* Twin-GAN - [Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.00946) \n* UGACH - [Unsupervised Generative Adversarial Cross-modal Hashing](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00358) \n* UGAN - [Enhancing Underwater Imagery using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1801.04011) \n* Unim2im - [Unsupervised Image-to-Image Translation with Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.02676) ([github](http:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FUnsup-Im2Im))\n* UNIT - [Unsupervised Image-to-image Translation Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1703.00848) ([github](https:\u002F\u002Fgithub.com\u002Fmingyuliutw\u002FUNIT))\n* Unrolled GAN - [Unrolled Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.02163) ([github](https:\u002F\u002Fgithub.com\u002Fpoolio\u002Funrolled_gan))\n* UT-SCA-GAN - [Spatial Image Steganography Based on Generative Adversarial Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.07939) \n* UV-GAN - [UV-GAN: Adversarial Facial UV Map Completion for Pose-invariant Face Recognition](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.04695) \n* VA-GAN - [Visual Feature Attribution using Wasserstein GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08998) \n* VAC+GAN  - [Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN), Multi Class Scenarios](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07751) \n* VAE-GAN - [Autoencoding beyond pixels using a learned similarity metric](https:\u002F\u002Farxiv.org\u002Fabs\u002F1512.09300) \n* VariGAN - [Multi-View Image Generation from a Single-View](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.04886) \n* VAW-GAN - [Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.00849) \n* VEEGAN - [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07761) ([github](https:\u002F\u002Fgithub.com\u002Fakashgit\u002FVEEGAN))\n* VGAN - [Generating Videos with Scene Dynamics](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.02612) ([github](https:\u002F\u002Fgithub.com\u002Fcvondrick\u002Fvideogan))\n* VGAN - [Generative Adversarial Networks as Variational Training of Energy Based Models](https:\u002F\u002Farxiv.org\u002Fabs\u002F1611.01799) ([github](https:\u002F\u002Fgithub.com\u002FShuangfei\u002Fvgan))\n* VGAN - [Text Generation Based on Generative Adversarial Nets with Latent Variable](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.00170) \n* ViGAN - [Image Generation and Editing with Variational Info Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.04568v1) \n* VIGAN - [VIGAN: Missing View Imputation with Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.06724) \n* VoiceGAN - [Voice Impersonation using Generative Adversarial Networks ](http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06840) \n* VOS-GAN - [VOS-GAN: Adversarial Learning of Visual-Temporal Dynamics for Unsupervised Dense Prediction in Videos](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09092) \n* VRAL - [Variance Regularizing Adversarial Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.00309) \n* WaterGAN - [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https:\u002F\u002Farxiv.org\u002Fabs\u002F1702.07392v1) \n* WaveGAN - [Synthesizing Audio with Generative Adversarial Networks ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1802.04208) \n* WaveletGLCA-GAN - [Global and Local Consistent Wavelet-domain Age Synthesis](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.07764) \n* weGAN - [Generative Adversarial Nets for Multiple Text Corpora](https:\u002F\u002Farxiv.org\u002Fabs\u002F1712.09127) \n* WGAN - [Wasserstein GAN](https:\u002F\u002Farxiv.org\u002Fabs\u002F1701.07875v2) ([github](https:\u002F\u002Fgithub.com\u002Fmartinarjovsky\u002FWassersteinGAN))\n* WGAN-CLS - [Text to Image Synthesis Using Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1805.00676) \n* WGAN-GP - [Improved Training of Wasserstein GANs](https:\u002F\u002Farxiv.org\u002Fabs\u002F1704.00028) ([github](https:\u002F\u002Fgithub.com\u002Figul222\u002Fimproved_wgan_training))\n* WGAN-L1 - [Subsampled Turbulence Removal Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.04418) \n* WS-GAN - [Weakly Supervised Generative Adversarial Networks for 3D Reconstruction ](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.10904) \n* X-GANs - [X-GANs: Image Reconstruction Made Easy for Extreme Cases](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04432) \n* XGAN - [XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.05139) \n* ZipNet-GAN - [ZipNet-GAN: Inferring Fine-grained Mobile Traffic Patterns via a Generative Adversarial Neural Network](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02413) \n* α-GAN - [Variational Approaches for Auto-Encoding Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.04987) ([github](https:\u002F\u002Fgithub.com\u002Fvictor-shepardson\u002Falpha-GAN))\n* β-GAN - [Annealed Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.07505) \n* Δ-GAN - [Triangle Generative Adversarial Networks](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.06548)","# The GAN Zoo 快速上手指南\n\nThe GAN Zoo 并非一个单一的代码库或可执行工具，而是一个**开源的 GAN 论文与项目索引列表**。它旨在帮助开发者追踪和查找各种命名独特的生成对抗网络（GAN）变体及其对应的原始论文和代码实现。\n\n本指南将指导你如何获取该列表数据，并如何利用它快速找到你需要的具体 GAN 模型代码进行运行。\n\n## 环境准备\n\n由于本项目本质是数据索引，无需安装特定的运行时环境。要使用其中的资源，你需要具备以下基础开发环境：\n\n*   **操作系统**: Windows, macOS 或 Linux\n*   **核心依赖**:\n    *   Git (用于克隆仓库)\n    *   Python 3.x (用于运行具体的 GAN 代码)\n    *   PyTorch 或 TensorFlow (根据你选择的具体 GAN 模型要求而定)\n*   **网络环境**: 能够访问 GitHub 和 arXiv。国内用户建议配置代理或使用加速服务以加快克隆速度。\n\n## 安装步骤\n\n你可以通过克隆 GitHub 仓库来获取完整的 GAN 列表数据（包含链接、标题等信息）。\n\n1.  **克隆仓库**\n    打开终端，执行以下命令：\n\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo.git\n    ```\n\n    *国内加速方案*: 如果克隆速度较慢，可以使用国内镜像源（如 Gitee 上的同步镜像，若有）或设置 git 代理：\n    ```bash\n    # 示例：设置 http 代理 (请替换为你的实际代理地址)\n    git config --global http.proxy http:\u002F\u002F127.0.0.1:7890\n    git config --global https.proxy https:\u002F\u002F127.0.0.1:7890\n    ```\n\n2.  **查看数据**\n    进入目录，核心数据存储在 `gans.tsv` 文件中，你可以用文本编辑器或 Excel 打开查看：\n\n    ```bash\n    cd the-gan-zoo\n    ls\n    # 主要文件：gans.tsv (包含所有 GAN 名称、论文标题、arXiv 链接和 GitHub 链接)\n    ```\n\n    你也可以直接在浏览器中查看在线表格版本：\n    [https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002Fgans.tsv](https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002Fgans.tsv)\n\n## 基本使用\n\nThe GAN Zoo 的使用流程是：**检索名称 -> 定位代码 -> 运行具体模型**。\n\n### 1. 检索目标模型\n假设你想寻找用于“文本生成图像”的 **AttnGAN** 模型：\n*   打开 `gans.tsv` 文件或在线表格。\n*   搜索关键词 `AttnGAN`。\n*   找到对应行，获取其 GitHub 代码库链接：`https:\u002F\u002Fgithub.com\u002Ftaoxugit\u002FAttnGAN`。\n\n### 2. 获取并运行具体模型\n根据检索到的链接，克隆该具体模型的代码库并运行。以下是以 **AttnGAN** 为例的操作：\n\n```bash\n# 克隆具体的 AttnGAN 项目\ngit clone https:\u002F\u002Fgithub.com\u002Ftaoxugit\u002FAttnGAN.git\ncd AttnGAN\n\n# 创建虚拟环境 (推荐)\npython -m venv venv\nsource venv\u002Fbin\u002Factivate  # Windows 用户使用: venv\\Scripts\\activate\n\n# 安装该模型所需的依赖 (具体依赖请参考该项目自身的 README)\npip install -r requirements.txt\n\n# 下载预训练模型或数据集 (遵循该项目说明)\n# 运行示例脚本\npython main.py --cfg configs\u002Fbird_attngan.yml\n```\n\n### 3. 探索其他模型\n你可以重复上述步骤，利用 `gans.tsv` 中的链接探索列表中数百种其他的 GAN 变体（如 CycleGAN, StyleGAN, BigGAN 等），每个模型都有其独立的仓库和使用说明。","某计算机视觉实验室的研究团队正致力于开发一种新型的医学图像超分辨率算法，急需调研最新的生成对抗网络（GAN）架构以寻找灵感。\n\n### 没有 the-gan-zoo 时\n- **检索效率低下**：研究人员需要在 arXiv、Google Scholar 等多个平台反复搜索，耗费数天时间才能拼凑出零散的 GAN 变体列表。\n- **命名混乱难辨**：面对如 \"AC-GAN\"、\"acGAN\"、\"ACGAN\" 等极其相似却代表不同论文的缩写，极易混淆概念或遗漏关键文献。\n- **缺乏系统分类**：难以快速筛选出针对特定任务（如 3D 重建或指纹生成）的专用模型，导致大量阅读无关论文，浪费算力资源进行无效复现。\n- **追踪更新困难**：每周都有新论文发布，手动维护本地表格耗时耗力，往往错过最新的技术突破点。\n\n### 使用 the-gan-zoo 后\n- **一站式获取清单**：团队直接访问 the-gan-zoo，瞬间获得包含数百种已命名 GAN 的完整索引，将前期调研时间从数天缩短至几小时。\n- **精准定位变体**：通过列表中清晰的标题链接和对应论文，迅速厘清同名异义或缩写相近的模型差异，准确锁定适合医学图像的 \"AffGAN\" 等架构。\n- **高效过滤匹配**：利用提供的 TSV 表格功能，按年份或关键词快速筛选出专注于“超分辨率”和“医学影像”的模型，直接复用开源代码链接加速实验。\n- **同步前沿动态**：依托社区持续更新的机制，团队能即时发现每周新增的 GAN 变体，确保算法设计始终站在技术最前沿。\n\nthe-gan-zoo 将杂乱无章的 GAN 命名迷宫转化为结构化的知识地图，让研究者从繁琐的文献搜集工作中解放出来，专注于核心算法创新。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fhindupuravinash_the-gan-zoo_b4ce9335.jpg","hindupuravinash","Avinash Hindupur","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fhindupuravinash_a15fddca.jpg","Machine Learning | Data | Developer","@idam-ai ","Hyderabad",null,"https:\u002F\u002Fwww.idam.ai","https:\u002F\u002Fgithub.com\u002Fhindupuravinash",[82],{"name":83,"color":84,"percentage":85},"Python","#3572A5",100,14696,2552,"2026-04-04T14:48:07","MIT",5,"","未说明",{"notes":94,"python":92,"dependencies":95},"该仓库（the-gan-zoo）并非一个可单独运行的 AI 工具或框架，而是一个收集了数百种不同生成对抗网络（GAN）论文、代码链接和名称的列表\u002F索引库。因此，它本身没有统一的运行环境、依赖库或硬件需求。具体的环境配置取决于用户选择列表中哪一个特定的 GAN 模型进行复现，需参考对应论文的官方代码仓库。",[],[15,14],[98,99,100],"machine-learning","gan","generative-adversarial-network","2026-03-27T02:49:30.150509","2026-04-06T23:59:36.818991",[104,109,114,119,124,129],{"id":105,"question_zh":106,"answer_zh":107,"source_url":108},20348,"如何按年份搜索或浏览 GAN 模型？","您可以使用仓库中的 `gans.tsv` 文件。该表格按时间顺序排列，支持通过年份、标题、名称、GitHub 仓库或 arXiv 链接进行搜索。链接地址：https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002Fgans.tsv","https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fissues\u002F10",{"id":110,"question_zh":111,"answer_zh":112,"source_url":113},20349,"在哪里可以找到特定论文的代码实现链接？","请查看仓库中的 `gans.tsv` 文件，其中包含了按名称、标题、年份、GitHub 仓库或 arXiv 链接整理的详细信息，可直接定位到代码实现。文件地址：https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fblob\u002Fmaster\u002Fgans.tsv","https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fissues\u002F63",{"id":115,"question_zh":116,"answer_zh":117,"source_url":118},20350,"项目是否提供对应 GitHub 项目的代码实现链接？","是的，维护者正在添加原始源代码链接；如果找不到原始代码，也会添加第三方实现。此外，用户也可以参考 https:\u002F\u002Fgithub.com\u002Ftdeboissiere\u002FDeepLearningImplementations 获取更多实现链接。","https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fissues\u002F21",{"id":120,"question_zh":121,"answer_zh":122,"source_url":123},20351,"README 中提到的 Deep Hunt 网站无法访问怎么办？","维护者测试后表示该网站目前工作正常，可能是暂时的网络问题或重定向延迟。如果链接跳转到 Medium 身份验证页面，请稍后重试或直接访问原网址。若问题持续，可重新提交 Issue。","https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fissues\u002F67",{"id":125,"question_zh":126,"answer_zh":127,"source_url":128},20352,"如何贡献新的 GAN 模型或其代码链接？","您可以通过提交 Issue 提供新的 GAN 模型信息（如 arXiv 论文链接或 GitHub 仓库地址）。维护者确认后会将其添加到列表中，例如 AttGAN、ALI 和 Kernel-GAN 都是通过这种方式加入的。","https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fissues\u002F54",{"id":130,"question_zh":131,"answer_zh":132,"source_url":133},20353,"如果发现某个 GAN 模型的代码链接缺失或错误该如何处理？","请在 Issue 中提供正确的 GitHub 仓库链接或 arXiv 论文地址。维护者会核实并更新列表，例如针对 ALI 和 Mocogan 的链接都是通过用户反馈补充完善的。","https:\u002F\u002Fgithub.com\u002Fhindupuravinash\u002Fthe-gan-zoo\u002Fissues\u002F47",[]]