{"id":13408285,"url":"https://github.com/nightrome/really-awesome-gan","last_synced_at":"2026-01-25T09:33:28.041Z","repository":{"id":37406241,"uuid":"87085729","full_name":"nightrome/really-awesome-gan","owner":"nightrome","description":"A list of papers on Generative Adversarial (Neural) Networks","archived":false,"fork":false,"pushed_at":"2025-08-24T07:57:51.000Z","size":106,"stargazers_count":3776,"open_issues_count":0,"forks_count":704,"subscribers_count":240,"default_branch":"master","last_synced_at":"2026-01-11T12:36:44.880Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nightrome.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-04-03T14:49:43.000Z","updated_at":"2026-01-09T07:41:37.000Z","dependencies_parsed_at":"2022-07-08T18:00:51.086Z","dependency_job_id":null,"html_url":"https://github.com/nightrome/really-awesome-gan","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/nightrome/really-awesome-gan","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nightrome%2Freally-awesome-gan","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nightrome%2Freally-awesome-gan/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nightrome%2Freally-awesome-gan/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nightrome%2Freally-awesome-gan/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nightrome","download_url":"https://codeload.github.com/nightrome/really-awesome-gan/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nightrome%2Freally-awesome-gan/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28750875,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-25T09:00:19.176Z","status":"ssl_error","status_checked_at":"2026-01-25T09:00:04.131Z","response_time":113,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-30T20:00:51.879Z","updated_at":"2026-01-25T09:33:28.034Z","avatar_url":"https://github.com/nightrome.png","language":null,"funding_links":[],"categories":["Uncategorized","Awesome Computer Vision","Applications Using GANs","Others","CV","Table of Contents","Adversarial Learning","Machine Learning or Deep Learning or AI or similar things whatever you like Lists","Other Lists","GAN","Multimodal, Vision-Language, and Generative AI"],"sub_categories":["Uncategorized","Other Applications","TeX Lists","Diffusion and Generative Models"],"readme":"# really-awesome-gan\nA list of papers and other resources on Generative Adversarial (Neural) Networks.\nThis site is maintained by Holger Caesar.\nTo complement or correct it, please contact me at holger-at-it-caesar.com or visit [it-caesar.com](http://www.it-caesar.com). Also checkout [really-awesome-semantic-segmentation](https://github.com/nightrome/really-awesome-semantic-segmentation) and our [COCO-Stuff dataset](https://github.com/nightrome/cocostuff).\n\n**NOTE:** Despite the enormous interest in this cite (~3000 visitors per month), I will no longer add new papers starting from November 2017. I feel that GANs have come from an exotic topic to the mainstream and an exhaustive list of all GAN papers is no more feasible or useful. However, I invite other people to continue this effort and reuse my list.\n\n## Contents\n- [Recommendations](#recommendations)\n- [Workshops](#workshops)\n- [Tutorials \u0026 Workshops \u0026 Blogs](#tutorials--workshops--blogs)\n- [Videos](#videos)\n- [Code](#code)\n- [Papers](#papers)\n  - [Overview](#overview)\n  - [Theory \u0026 Machine Learning](#theory--machine-learning)\n  - [Applied Vision](#applied-vision)\n  - [Applied Other](#applied-other)\n  - [Humor](#humor)\n  \n## Recommendations\n\u003cul\u003e\n\u003cli\u003eBeyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis \u003ca href=\"https://arxiv.org/abs/1704.04086\"\u003e[arXiv]\u003c/a\u003e \n\u003cimg src=\"http://it-caesar.com/github/beyond-face-rotation.png\" alt=\"Beyond face rotation\"\u003e\u003c/li\u003e\n\n\u003cli\u003ePose Guided Person Image Generation \u003ca href=\"https://arxiv.org/abs/1705.09368\"\u003e[arXiv]\u003c/a\u003e \n\u003cimg src=\"http://it-caesar.com/github/pose-guided-person.png\" alt=\"Pose guided person\"\u003e\u003c/li\u003e\n\n\u003cli\u003eUnpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks \u003ca href=\"https://arxiv.org/abs/1703.10593\"\u003e[arXiv]\u003c/a\u003e  \n\u003cimg src=\"http://it-caesar.com/github/cycle-gan.png\" alt=\"Cycle GAN\"\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\n# Tutorials \u0026 Workshops \u0026 Blogs\n- Columbia Advanced Machine Learning Seminar\n  - New Progress on GAN Theory and Practice [[Blog]](https://casmls.github.io/general/2017/04/13/gan.html)\n  - Implicit Generative Models — What are you GAN-na do? [[Blog]](https://casmls.github.io/general/2017/05/24/ligm.html)\n- How to Train a GAN? Tips and tricks to make GANs work [[Blog]](https://github.com/soumith/ganhacks)\n- NIPS 2016 Tutorial: Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.00160)\n- NIPS 2016 Workshop on Adversarial Training [[Web]](https://sites.google.com/site/nips2016adversarial/) [[Blog]](http://www.inference.vc/my-summary-of-adversarial-training-nips-workshop/)\n- On the intuition behind deep learning \u0026 GANs — towards a fundamental understanding [[Blog]](https://blog.waya.ai/introduction-to-gans-a-boxing-match-b-w-neural-nets-b4e5319cc935)\n- OpenAI - Generative Models [[Blog]](https://openai.com/blog/generative-models/)\n- SimGANs - a game changer in unsupervised learning, self driving cars, and more [[Blog]](https://blog.waya.ai/simgans-applied-to-autonomous-driving-5a8c6676e36b)\n- Deep Diving into GANs: from theory to production (EuroScipy 2018) [[GitHub]](https://github.com/zurutech/gans-from-theory-to-production) \n\n# Books\n- GANs in Action: Deep learning with Generative Adversarial Networks [[Book]](https://www.manning.com/books/gans-in-action)\n\n# Videos\n- Generative Adversarial Networks by Ian Goodfellow [[Video]](https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Generative-Adversarial-Networks)\n- Tutorial on Generative Adversarial Networks by Mark Chang [[Video]](https://www.youtube.com/playlist?list=PLeeHDpwX2Kj5Ugx6c9EfDLDojuQxnmxmU)\n- Deep Diving into GANs: From Theory to Production (EuroSciPy 2018) by Michele De Simoni, Paolo Galeone [[Video]](https://www.youtube.com/watch?v=CePrdabdtxw) \n\n# Code\n- Cleverhans: A library for benchmarking vulnerability to adversarial examples [[Code]](https://github.com/openai/cleverhans) [[Blog]](http://cleverhans.io/)\n- Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch) [[Blog]](https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f) [[Code]](https://github.com/devnag/pytorch-generative-adversarial-networks)\n- Generative Models: Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow [[Code]](https://github.com/wiseodd/generative-models)\n- Reproduction of the GANs paper (MNIST) in 100 lines of PyTorch code  [[Blog]](https://papers-100-lines.medium.com/generative-adversarial-networks-in-100-lines-of-code-516f09d1790a) [[Code]](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Generative_Adversarial_Networks)\n- Reproduction of results from the paper *Conditional Generative Adversarial Nets* in 100 lines of PyTorch code  [[Code]](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Conditional_Generative_Adversarial_Nets)\n- Reproduction of results from the paper *Improved Techniques for Training GANs* in 100 lines of PyTorch code  [[Code]](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Improved_Techniques_for_Training_GANs)\n- Reproduction of results from the *LSGAN* paper in 100 lines of PyTorch code  [[Code]](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Least_Squares_Generative_Adversarial_Networks)\n- Reproduction of results from the *WGAN* paper in 100 lines of PyTorch code  [[Code]](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Wasserstein_GAN)\n- Reproduction of results from the *pix2pix* paper in 100 lines of PyTorch code  [[Code]](https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Image_to_Image_Translation_with_Conditional_Adversarial_Nets)\n\n# Papers\n## Overview\n- Generative Adversarial Networks: An Overview [[arXiv]](https://arxiv.org/abs/1710.07035)\n\n## Theory \u0026 Machine Learning\n- A Classification-Based Perspective on GAN Distributions [[arXiv]](https://arxiv.org/abs/1711.00970)\n- A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models [[arXiv]](https://arxiv.org/abs/1611.03852)\n- A General Retraining Framework for Scalable Adversarial Classification [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_2.pdf)\n- Activation Maximization Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1703.02000)\n- AdaGAN: Boosting Generative Models [[arXiv]](https://arxiv.org/abs/1701.02386)\n- Adversarial Autoencoders [[arXiv]](https://arxiv.org/abs/1511.05644)\n- Adversarial Discriminative Domain Adaptation [[arXiv]](https://arxiv.org/abs/1702.05464)\n- Adversarial Generator-Encoder Networks [[arXiv]](https://arxiv.org/pdf/1704.02304.pdf)\n- Adversarial Feature Learning [[arXiv]](https://arxiv.org/abs/1605.09782) [[Code]](https://github.com/wiseodd/generative-models)\n- Adversarially Learned Inference [[arXiv]](https://arxiv.org/abs/1606.00704) [[Code]](https://github.com/wiseodd/generative-models)\n- AE-GAN: adversarial eliminating with GAN [[arXiv]](https://arxiv.org/abs/1707.05474)\n- An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks [[arXiv]](https://arxiv.org/abs/1702.02382)\n- APE-GAN: Adversarial Perturbation Elimination with GAN [[arXiv]](https://arxiv.org/abs/1707.05474)\n- Associative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.06953)\n- Autoencoding beyond pixels using a learned similarity metric [[arXiv]](https://arxiv.org/abs/1512.09300)\n- Bayesian Conditional Generative Adverserial Networks [[arXiv]](https://arxiv.org/abs/1706.05477)\n- Bayesian GAN [[arXiv]](https://arxiv.org/abs/1705.09558)\n- BEGAN: Boundary Equilibrium Generative Adversarial Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_4.pdf) [[arXiv]](https://arxiv.org/abs/1703.10717) [[Code]](https://github.com/wiseodd/generative-models)\n- Binary Generative Adversarial Networks for Image Retrieval [[arXiv]](https://arxiv.org/abs/1708.04150)\n- Boundary-Seeking Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.08431) [[Code]](https://github.com/wiseodd/generative-models)\n- CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training [[arXiv]](https://arxiv.org/abs/1709.02023)\n- Class-Splitting Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.07359)\n- Comparison of Maximum Likelihood and GAN-based training of Real NVPs [[arXiv]](https://arxiv.org/abs/1705.05263)\n- Conditional CycleGAN for Attribute Guided Face Image Generation [[arXiv]](https://arxiv.org/abs/1705.09966)\n- Conditional Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1411.1784) [[Code]](https://github.com/wiseodd/generative-models)\n- Connecting Generative Adversarial Networks and Actor-Critic Methods [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_1.pdf)\n- Continual Learning in Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1705.08395)\n- C-RNN-GAN: Continuous recurrent neural networks with adversarial training [[arXiv]](https://arxiv.org/abs/1611.09904)\n- CM-GANs: Cross-modal Generative Adversarial Networks for Common Representation Learning [[arXiv]](https://arxiv.org/abs/1710.05106)\n- Cooperative Training of Descriptor and Generator Networks [[arXiv]](https://arxiv.org/abs/1609.09408)\n- Coupled Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1606.07536) [[Code]](https://github.com/wiseodd/generative-models)\n- Dualing GANs [[arXiv]](https://arxiv.org/abs/1706.06216)\n- Deep and Hierarchical Implicit Models [[arXiv]](https://arxiv.org/abs/1702.08896)\n- Energy-based Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1609.03126) [[Code]](https://github.com/wiseodd/generative-models)\n- Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank Decomposition [[Paper]](https://ieeexplore.ieee.org/document/10732016) [[Code]](https://github.com/PrasannaPulakurthi/MMD-PMish-NAS-GAN) [[Website]](https://prasannapulakurthi.github.io/MMD-PMish-NAS-GAN/) [[YouTube]](https://youtu.be/yejnLOO2VaI) [[Demo]](https://huggingface.co/spaces/prasannareddyp/MMD-PMish-NAS-GAN)\n- Enhancing GAN Performance Through Neural Architecture Search and Tensor Decomposition [[Paper]](https://ieeexplore.ieee.org/document/10446488) [[PDF]](https://prasannapulakurthi.github.io/papers/PDFs/2024_ICASSP_GANs-Tensor-Decomposition.pdf) [[Code]](https://github.com/PrasannaPulakurthi/MMD-AdversarialNAS-GAN)\n- Explaining and Harnessing Adversarial Examples [[arXiv]](https://arxiv.org/abs/1412.6572)\n- Flow-GAN: Bridging implicit and prescribed learning in generative models [[arXiv]](https://arxiv.org/abs/1705.08868)\n- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization [[arXiv]](https://arxiv.org/abs/1606.00709) [[Code]](https://github.com/wiseodd/generative-models)\n- Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking [[arXiv]](https://arxiv.org/abs/1704.04865)\n- Generalization and Equilibrium in Generative Adversarial Nets (GANs) [[arXiv]](https://arxiv.org/abs/1703.00573)\n- Generating images with recurrent adversarial networks [[arXiv]](https://arxiv.org/abs/1602.05110)\n- Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1406.2661) [[Code]](https://github.com/goodfeli/adversarial) [[Code]](https://github.com/wiseodd/generative-models)\n- Generative Adversarial Networks as Variational Training of Energy Based Models [[arXiv]](https://arxiv.org/abs/1611.01799)\n- Generative Adversarial Networks with Inverse Transformation Unit [[arXiv]](https://arxiv.org/abs/1709.09354)\n- Generative Adversarial Parallelization [[arXiv]](https://arxiv.org/abs/1612.04021) [[Code]](https://github.com/wiseodd/generative-models)\n- Generative Adversarial Residual Pairwise Networks for One Shot Learning [[arXiv]](https://arxiv.org/abs/1703.08033)\n- Generative Adversarial Structured Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_14.pdf)\n- Generative Cooperative Net for Image Generation and Data Augmentation [[arXiv]](https://arxiv.org/abs/1705.02887)\n- Generative Moment Matching Networks [[arXiv]](https://arxiv.org/abs/1502.02761) [[Code]](https://github.com/yujiali/gmmn)\n- Generative Semantic Manipulation with Contrasting GAN [[arXiv]](https://arxiv.org/abs/1708.00315)\n- Geometric GAN [[arXiv]](https://arxiv.org/abs/1705.02894)\n- Good Semi-supervised Learning that Requires a Bad GAN [[arXiv]](https://arxiv.org/abs/1705.09783)\n- Gradient descent GAN optimization is locally stable [[arXiv]](https://arxiv.org/abs/1706.04156)\n- How to Train Your DRAGAN [[arXiv]](https://arxiv.org/abs/1705.07215)\n- Image Quality Assessment Techniques Show Improved Training and Evaluation of Autoencoder Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1708.02237)\n- Improved Semi-supervised Learning with GANs using Manifold Invariances [[arXiv]](https://arxiv.org/abs/1705.08850)\n- Improved Techniques for Training GANs [[arXiv]](https://arxiv.org/abs/1606.03498) [[Code]](https://github.com/openai/improved-gan)\n- Improved Training of Wasserstein GANs [[arXiv]](https://arxiv.org/abs/1704.00028) [[Code]](https://github.com/wiseodd/generative-models)\n- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1606.03657) [[Code]](https://github.com/wiseodd/generative-models)\n- Inverting The Generator Of A Generative Adversarial Network [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_9.pdf)\n- It Takes (Only) Two: Adversarial Generator-Encoder Networks [[arXiv]](https://arxiv.org/abs/1704.02304)\n- KGAN: How to Break The Minimax Game in GAN [[arXiv]](https://arxiv.org/abs/1711.01744)\n- Learning in Implicit Generative Models [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_10.pdf)\n- Learning Loss for Knowledge Distillation with Conditional Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.00513)\n- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.05192) [[Code]](https://github.com/wiseodd/generative-models)\n- Learning Texture Manifolds with the Periodic Spatial GAN [[arXiv]](https://arxiv.org/abs/1705.06566)\n- Least Squares Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.04076) [[Code]](https://github.com/wiseodd/generative-models)\n- Linking Generative Adversarial Learning and Binary Classification [[arXiv]](https://arxiv.org/abs/1709.01509)\n- Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities [[arXiv]](https://arxiv.org/abs/1701.06264)\n- LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation [[arXiv]](https://arxiv.org/abs/1703.01560)\n- MAGAN: Margin Adaptation for Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.03817) [[Code]](https://github.com/wiseodd/generative-models)\n- Maximum-Likelihood Augmented Discrete Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.07983)\n- McGan: Mean and Covariance Feature Matching GAN [[arXiv]](https://arxiv.org/abs/1702.08398)\n- Message Passing Multi-Agent GANs [[arXiv]](https://arxiv.org/abs/1612.01294)\n- MMD GAN: Towards Deeper Understanding of Moment Matching Network [[arXiv]](https://arxiv.org/abs/1705.08584)\n- Mode Regularized Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.02136) [[Code]](https://github.com/wiseodd/generative-models)\n- Multi-Agent Diverse Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.02906)\n- Multi-Generator Gernerative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1708.02556)\n- Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models [[arXiv]](https://arxiv.org/abs/1705.10843)\n- On Convergence and Stability of GANs [[arXiv]](https://arxiv.org/abs/1705.07215)\n- On the effect of Batch Normalization and Weight Normalization in Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.03971)\n- On the Quantitative Analysis of Decoder-Based Generative Models [[arXiv]](https://arxiv.org/abs/1611.04273)\n- Optimizing the Latent Space of Generative Networks [[arXiv]](https://arxiv.org/abs/1707.05776)\n- Parametrizing filters of a CNN with a GAN [[arXiv]](https://arxiv.org/abs/1710.11386)\n- PixelGAN Autoencoders [[arXiv]](https://arxiv.org/abs/1706.00531)\n- Progressive Growing of GANs for Improved Quality, Stability, and Variation [[arXiv]](https://arxiv.org/abs/1710.10196) [[Code]](https://github.com/tkarras/progressive_growing_of_gans)\n- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[arXiv]](https://arxiv.org/abs/1706.01805)\n- SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient [[arXiv]](https://arxiv.org/abs/1609.05473)\n- Simple Black-Box Adversarial Perturbations for Deep Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_11.pdf)\n- Softmax GAN [[arXiv]](https://arxiv.org/abs/1704.06191)\n- Stabilizing Training of Generative Adversarial Networks through Regularization [[arXiv]](https://arxiv.org/abs/1705.09367)\n- Stacked Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.04357)\n- Statistics of Deep Generated Images [[arXiv]](https://arxiv.org/abs/1708.02688)\n- Structured Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1711.00889)\n- Tensorizing Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1710.10772)\n- The Cramer Distance as a Solution to Biased Wasserstein Gradients [[arXiv]](https://arxiv.org/abs/1705.10743)\n- Towards Understanding Adversarial Learning for Joint Distribution Matching [[arXiv]](https://arxiv.org/abs/1709.01215)\n- Training generative neural networks via Maximum Mean Discrepancy optimization [[arXiv]](https://arxiv.org/abs/1505.03906)\n- Triple Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1703.02291)\n- Unrolled Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.02163)\n- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1511.06434) [[Code]](https://github.com/Newmu/dcgan_code) [[Code]](https://github.com/pytorch/examples/tree/master/dcgan) [[Code]](https://github.com/carpedm20/DCGAN-tensorflow) [[Code]](https://github.com/soumith/dcgan.torch) [[Code]](https://github.com/jacobgil/keras-dcgan)\n- Wasserstein GAN [[arXiv]](https://arxiv.org/abs/1701.07875) [[Code]](https://github.com/martinarjovsky/WassersteinGAN) [[Code]](https://github.com/wiseodd/generative-models)\n\n## Applied Vision\n- 3D Object Reconstruction from a Single Depth View with Adversarial Learning [[arXiv]](https://arxiv.org/abs/1708.07969)\n- 3D Shape Induction from 2D Views of Multiple Objects [[arXiv]](https://arxiv.org/abs/1612.05872)\n- A step towards procedural terrain generation with GANs [[arXiv]](https://arxiv.org/abs/1707.03383) [[Code]](https://github.com/christopher-beckham/gan-heightmaps)\n- Abnormal Event Detection in Videos using Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1708.09644)\n- Adversarial Generation of Training Examples for Vehicle License Plate Recognition [[arXiv]](https://arxiv.org/abs/1707.03124)\n- Adversarial nets with perceptual losses for text-to-image synthesis [[arXiv]](https://arxiv.org/abs/1708.09321)\n- Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB [[arXiv]](https://arxiv.org/abs/1709.00265)\n- Adversarial Networks for the Detection of Aggressive Prostate Cancer [[arXiv]](https://arxiv.org/abs/1702.08014)\n- Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation [[arXiv]](https://arxiv.org/pdf/1705.00389.pdf)\n- Adversarial Training For Sketch Retrieval [[arXiv]](https://arxiv.org/abs/1607.02748)\n- Aesthetic-Driven Image Enhancement by Adversarial Learning [[arXiv]](https://arxiv.org/abs/1707.05251)\n- Age Progression / Regression by Conditional Adversarial Autoencoder [[arXiv]](https://arxiv.org/abs/1702.08423)\n- AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.01400)\n- Amortised MAP Inference for Image Super-resolution [[arXiv]](https://arxiv.org/abs/1610.04490)\n- Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network [[arXiv]](https://arxiv.org/abs/1811.00344) [[Code]](https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw)\n- A Novel Approach to Artistic Textual Visualization via GAN [[arXiv]](https://arxiv.org/abs/1710.10553)\n- Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification [[arXiv]](https://arxiv.org/abs/1709.03654)\n- Arbitrary Facial Attribute Editing: Only Change What You Want [[arXiv]](https://arxiv.org/abs/1711.10678) [[Code]](https://github.com/LynnHo/AttGAN-Tensorflow)\n- ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1709.00938)\n- ArtGAN: Artwork Synthesis with Conditional Categorial GANs [[arXiv]](https://arxiv.org/abs/1702.03410)\n- Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data [[arXiv]](https://arxiv.org/abs/1711.02010)\n- Auto-Encoder Guided GAN for Chinese Calligraphy Synthesis [[arXiv]](https://arxiv.org/abs/1706.08789)\n- Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1705.01908)\n- Automatic Liver Segmentation Using an Adversarial Image-to-Image Network [[arXiv]](https://arxiv.org/abs/1707.08037)\n- Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis [[arXiv]](https://arxiv.org/abs/1704.04086)\n- CAN: Creative Adversarial Networks Generating “Art” by Learning About Styles and Deviating from Style Norms [[arXiv]](https://arxiv.org/abs/1706.07068)\n- CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition [[arXiv]](https://arxiv.org/abs/1811.07441) [[Code]](https://github.com/nschor/CompoNet)\n- Compressed Sensing MRI Reconstruction with Cyclic Loss in Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.00753)\n- Conditional Adversarial Network for Semantic Segmentation of Brain Tumor [[arXiv]](https://arxiv.org/abs/1708.05227)\n- Conditional generative adversarial nets for convolutional face generation [[Paper]](http://www.foldl.me/uploads/2015/conditional-gans-face-generation/paper.pdf)\n- Conditional Image Synthesis with Auxiliary Classifier GANs [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_7.pdf) [[arXiv]](https://arxiv.org/abs/1610.09585) [[Code]](https://github.com/wiseodd/generative-models)\n- Contextual RNN-GANs for Abstract Reasoning Diagram Generation [[arXiv]](https://arxiv.org/abs/1609.09444)\n- Controllable Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1708.00598)\n- Creatism: A deep-learning photographer capable of creating professional work [[arXiv]](https://arxiv.org/abs/1707.03491)\n- Crossing Nets: Combining GANs and VAEs with a Shared Latent Space for Hand Pose Estimation [[arXiv]](https://arxiv.org/abs/1702.03431)\n- CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training [[arXiv]](https://arxiv.org/abs/1703.10155)\n- Data Augmentation in Classification using GAN [[arXiv]](https://arxiv.org/abs/1711.00648)\n- Deep Generative Adversarial Compression Artifact Removal [[arXiv]](https://arxiv.org/abs/1704.02518)\n- Deep Generative Adversarial Networks for Compressed Sensing (GANCS) Automates MRI [[arXiv]](https://arxiv.org/abs/1706.00051)\n- Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis [[arXiv]](https://arxiv.org/abs/1708.00129)\n- Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks [[arXiv]](https://arxiv.org/abs/1506.05751) [[Code]](https://github.com/facebook/eyescream) [[Blog]](http://soumith.ch/eyescream/)\n- Deep multi-scale video prediction beyond mean square error [[arXiv]](https://arxiv.org/abs/1511.05440) [[Code]](https://github.com/dyelax/Adversarial_Video_Generation)\n- Deep Unsupervised Representation Learning for Remote Sensing Images [[arXiv]](https://arxiv.org/abs/1612.08879)\n- DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data [[arXiv]](https://arxiv.org/abs/1706.02071)\n- Depth Structure Preserving Scene Image Generation [[arXiv]](https://arxiv.org/abs/1706.00212)\n- DualGAN: Unsupervised Dual Learning for Image-to-Image Translation [[arXiv]](https://arxiv.org/abs/1704.02510) [[Code]](https://github.com/wiseodd/generative-models)\n- Dual Motion GAN for Future-Flow Embedded Video Prediction [[arXiv]](https://arxiv.org/abs/1708.00284)\n- Efficient Super Resolution For Large-Scale Images Using Attentional GAN [[arXiv]](https://arxiv.org/abs/1812.04821) [[Thesis]](https://digitalcommons.wpi.edu/etd-theses/1256/) [[Thesis]](https://www.wpi.edu/news/announcements/data-science-ms-thesis-presentation-xiaozhou-zou)\n- ExprGAN: Facial Expression Editing with Controllable Expression Intensity [[arXiv]](https://arxiv.org/abs/1709.03842)\n- Face Aging With Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.01983)\n- Face Transfer with Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1710.06090)\n- Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1710.04835)\n- Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.05392)\n- From source to target and back: symmetric bi-directional adaptive GAN [[arXiv]](https://arxiv.org/abs/1705.08824)\n- Full Resolution Image Compression with Recurrent Neural Networks [[arXiv]](https://arxiv.org/abs/1608.05148)\n- GANs for Biological Image Synthesis [[arXiv]](https://arxiv.org/abs/1708.04692)\n- GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data [[arXiv]](https://arxiv.org/abs/1705.04932) [[Code]](https://github.com/Prinsphield/GeneGAN)\n- Generate Identity-Preserving Faces by Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.03227)\n- Generate To Adapt: Aligning Domains using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.01705)\n- Generative Adversarial Graph Convolutional Networks for Human Action Synthesis [[arXiv]](https://arxiv.org/abs/2110.11191) [[Code]](https://github.com/DegardinBruno/Kinetic-GAN)\n- Generative Adversarial Models for People Attribute Recognition in Surveillance [[arXiv]](https://arxiv.org/abs/1707.02240)\n- Generative Adversarial Network based on Resnet for Conditional Image Restoration [[arxiv]](https://arxiv.org/abs/1707.04881)\n- Generative Adversarial Network-based Synthesis of Visible Faces from Polarimetric Thermal Faces [[arXiv]](https://arxiv.org/abs/1708.02681)\n- Generative Adversarial Networks for Multimodal Representation Learning in Video Hyperlinking [[arXiv]](https://arxiv.org/abs/1705.05103)\n- Generative Adversarial Text to Image Synthesis [[arXiv]](https://arxiv.org/abs/1605.05396) [[Code]](https://github.com/paarthneekhara/text-to-image)\n- Generative Visual Manipulation on the Natural Image Manifold [[Project]](http://www.eecs.berkeley.edu/~junyanz/projects/gvm/) [[Youtube]](https://youtu.be/9c4z6YsBGQ0) [[Paper]](https://arxiv.org/abs/1609.03552) [[Code]](https://github.com/junyanz/iGAN)\n- Global-to-Local Generative Model for 3D Shapes [[Project]](http://vcc.szu.edu.cn/research/2018/G2L)[[Code]](https://github.com/Hao-HUST/G2LGAN)\n- GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks [[arXiv]](https://arxiv.org/abs/1710.00962)\n- GP-GAN: Towards Realistic High-Resolution Image Blending [[arXiv]](https://arxiv.org/abs/1703.07195)\n- Guiding InfoGAN with Semi-Supervision [[arXiv]](https://arxiv.org/abs/1707.04487)\n- How to Fool Radiologists with Generative Adversarial Networks? A Visual Turing Test for Lung Cancer Diagnosis [[arXiv]](https://arxiv.org/abs/1710.09762)\n- Hierarchical Detail Enhancing Mesh-Based Shape Generation with 3D Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1709.07581)\n- High-Quality Face Image SR Using Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.00737)\n- High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks [[arXiv]](https://arxiv.org/abs/1710.10182)\n- Image De-raining Using a Conditional Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1701.05957)\n- Image Generation and Editing with Variational Info Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.04568)\n- Image-to-Image Translation with Conditional Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.07004) [[Code]](https://github.com/phillipi/pix2pix)\n- Improved Adversarial Systems for 3D Object Generation and Reconstruction [[arXiv]](https://arxiv.org/abs/1707.09557) [[Code]](https://github.com/EdwardSmith1884/3D-IWGAN)\n- Improving Heterogeneous Face Recognition with Conditional Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.02848)\n- Improving image generative models with human interactions [[arXiv]](https://arxiv.org/abs/1709.10459)\n- Imitating Driver Behavior with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.06699)\n- Interactive 3D Modeling with a Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1706.05170)\n- Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.02255)\n- Invertible Conditional GANs for image editing [[arXiv]](https://arxiv.org/abs/1611.06355) [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_8.pdf)\n- Joint Discriminative and Generative Learning for Person Re-identification [[Project]](http://zdzheng.xyz/DG-Net/) [[Paper]](https://arxiv.org/abs/1904.07223) [[YouTube]](https://www.youtube.com/watch?v=ubCrEAIpQs4) [[Bilibili]](https://www.bilibili.com/video/av51439240) [[Poster]](http://zdzheng.xyz/images/DGNet_poster.pdf) [[Code]](https://github.com/NVlabs/DG-Net)\n- Label Denoising Adversarial Network (LDAN) for Inverse Lighting of Face Images [[arXiv]](https://arxiv.org/abs/1709.01993)\n- Learning a Driving Simulator [[arXiv]](https://arxiv.org/abs/1608.01230)\n- Learning a Generative Adversarial Network for High Resolution Artwork Synthesis [[arXiv]](https://arxiv.org/abs/1708.09533)\n- Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling [[arXiv]](https://arxiv.org/abs/1610.07584)\n- Learning from Simulated and Unsupervised Images through Adversarial Training [[arXiv]](https://arxiv.org/abs/1612.07828)\n- Learning to Discover Cross-Domain Relations with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.05192)\n- Learning to Generate Chairs with Generative Adversarial Nets [[arXiv]](https://arxiv.org/abs/1705.10413)\n- Learning to Generate Time-Lapse Videos Using Multi-Stage Dynamic Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.07592)\n- Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [[arXiv]](https://arxiv.org/abs/1708.00961)\n- MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification [[arXiv]](https://arxiv.org/abs/1612.08879)\n- Megapixel Size Image Creation using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.00082)\n- Microscopy Cell Segmentation via Adversarial Neural Networks [[arXiv]](https://arxiv.org/abs/1709.05860)\n- MoCoGAN: Decomposing Motion and Content for Video Generation [[arXiv]](https://arxiv.org/abs/1707.04993)\n- Multi-view Generative Adversarial Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_13.pdf)\n- Neural Photo Editing with Introspective Adversarial Networks [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_15.pdf) [[arXiv]](https://arxiv.org/abs/1609.07093)\n- Neural Stain-Style Transfer Learning using GAN for Histopathological Images [[arXiv]](https://arxiv.org/abs/1710.08543)\n- Outline Colorization through Tandem Adversarial Networks [[arXiv]](https://arxiv.org/abs/1704.08834)\n- Perceptual Adversarial Networks for Image-to-Image Transformation [[arXiv]](https://arxiv.org/abs/1706.09138)\n- Perceptual Generative Adversarial Networks for Small Object Detection [[arXiv]](https://arxiv.org/abs/1706.05274)\n- Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1609.04802)\n- Pose Guided Person Image Generation [[arXiv]](https://arxiv.org/abs/1705.09368)\n- Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1604.04382)\n- Recurrent Topic-Transition GAN for Visual Paragraph Generation [[arXiv]](https://arxiv.org/abs/1703.07022)\n- RenderGAN: Generating Realistic Labeled Data [[arXiv]](https://arxiv.org/abs/1611.01331)\n- Representation Learning and Adversarial Generation of 3D Point Clouds [[arXiv]](https://arxiv.org/abs/1707.02392)\n- Retinal Vasculature Segmentation Using Local Saliency Maps and Generative Adversarial Networks For Image Super Resolution [[arXiv]](https://arxiv.org/abs/1710.04783)\n- Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1706.09318)\n- SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.08788)\n- SalGAN: Visual Saliency Prediction with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1701.01081v2)\n- SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation [[arXiv]](https://arxiv.org/abs/1706.01805)\n- SeGAN: Segmenting and Generating the Invisible [[arXiv]](https://arxiv.org/abs/1703.10239)\n- Semantic Image Inpainting with Deep Generative Models [[arXiv]](https://arxiv.org/abs/1607.07539)\n- EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning [[arXiv]](https://arxiv.org/abs/1901.00212) [[Code]](https://github.com/knazeri/edge-connect)\n- Semantic Image Synthesis via Adversarial Learning [[arXiv]](https://arxiv.org/abs/1707.06873)\n- Semantic Segmentation using Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.08408)\n- Semantically Decomposing the Latent Spaces of Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1705.07904)\n- Semi-Latent GAN: Learning to generate and modify facial images from attributes [[arXiv]](https://arxiv.org/abs/1704.02166)\n- Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.06430)\n- Sharpness-aware Low dose CT denoising using conditional generative adversarial network [[arXiv]](https://arxiv.org/abs/1708.06453)\n- Simultaneously Color-Depth Super-Resolution with Conditional Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1708.09105)\n- SingleGAN: Image-to-Image Translation by a Single-Generator Network using Multiple Generative Adversarial Learning [[arXiv]](https://arxiv.org/abs/1810.04991) [[Code]](https://github.com/Xiaoming-Yu/SingleGAN)\n- Socially-compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning [[arXiv]](https://arxiv.org/abs/1710.02543)\n- StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.03242)\n- StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1710.10916)\n- Style Transfer for Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN [[arXiv]](https://arxiv.org/abs/1706.03319)\n- Supervised Adversarial Networks for Image Saliency Detection [[arXiv]](https://arxiv.org/abs/1704.07242)\n- Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs) [[arXiv]](https://arxiv.org/abs/1707.09747)\n- Synthesizing Filamentary Structured Images with GANs [[arXiv]](https://arxiv.org/abs/1706.02185)\n- Synthetic Iris Presentation Attack using iDCGAN [[arXiv]](https://arxiv.org/abs/1710.10565)\n- Synthetic Medical Images from Dual Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.01872)\n- TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1703.06412)\n- Temporal Generative Adversarial Nets with Singular Value Clipping [[arXiv]](https://arxiv.org/abs/1611.06624)\n- TextureGAN: Controlling Deep Image Synthesis with Texture Patches [[arXiv]](https://arxiv.org/abs/1706.02823)\n- Texture Synthesis with Spatial Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1611.08207v3) [[Code]](https://github.com/ubergmann/spatial_gan)\n- Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language [[arXiv]](https://arxiv.org/abs/1810.11919) [[Code]](https://github.com/woozzu/tagan)\n- The Conditional Analogy GAN: Swapping Fashion Articles on People Images [[arXiv]](https://arxiv.org/abs/1709.04695)\n- TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation [[Paper]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720222.pdf)\n- TopoGAN: A Topology-Aware Generative Adversarial Network [[Paper]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123480120.pdf)\n- Towards Adversarial Retinal Image Synthesis [[arXiv]](https://arxiv.org/abs/1701.08974) [[Code]](https://github.com/costapt/vess2ret) [[Demo]](http://vess2ret.inesctec.pt/retina)\n- Towards Diverse and Natural Image Descriptions via a Conditional GAN [[arXiv]](https://arxiv.org/abs/1703.06029)\n- Towards the Automatic Anime Characters Creation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1708.05509)\n- UGAN: Enhancing Underwater Imagery using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1801.04011)\n- Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro [[arXiv]](https://arxiv.org/abs/1701.07717)[[Code]](https://github.com/layumi/Person-reID_GAN)\n- Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.10593)\n- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1511.06390)\n- Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery [[arXiv]](https://arxiv.org/abs/1703.05921)\n- Unsupervised Cross-Domain Image Generation [[arXiv]](https://arxiv.org/abs/1611.02200)\n- Unsupervised Diverse Colorization via Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1702.06674)\n- Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1612.05424)\n- Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.09798)\n- VIGAN: Missing View Imputation with Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1708.06724)\n- WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images [[arXiv]](https://arxiv.org/abs/1702.07392)\n- Weakly Supervised Generative Adversarial Networks for 3D Reconstruction [[arXiv]](https://arxiv.org/abs/1705.10904)\n- [TomoGAN: Low-Dose X-Ray Tomography with Generative Adversarial Networks] [[scholar]](https://scholar.google.ca/scholar?hl=en\u0026as_sdt=0%2C5\u0026q=TomoGAN%3A+Low-Dose+X-Ray+Tomography+with+Generative+Adversarial+Networks\u0026btnG=) [[arXiv]](https://arxiv.org/abs/1902.07582) \n\n## Applied Other\n- Adversarial Generation of Natural Language [[arXiv]](https://arxiv.org/abs/1705.10929)\n- Adversarial Ranking for Language Generation [[arXiv]](https://arxiv.org/abs/1705.11001)\n- Adversarial Training Methods for Semi-Supervised Text Classification [[arXiv]](https://arxiv.org/abs/1605.07725) [[Paper]](https://c4209155-a-62cb3a1a-s-sites.googlegroups.com/site/nips2016adversarial/WAT16_paper_12.pdf)\n- A Generative Model for Volume Rendering [[arXiv]](A Generative Model for Volume Rendering)\n- ChemGAN challenge for drug discovery: can AI reproduce natural chemical diversity? [[arXiv]](https://arxiv.org/abs/1708.08227)\n- Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN [[arXiv]](https://arxiv.org/abs/1702.05983)\n- Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.06490)\n- Language Generation with Recurrent Generative Adversarial Networks without Pre-training [[arXiv]](https://arxiv.org/abs/1706.01399)\n- Learning to Protect Communications with Adversarial Neural Cryptography [[arXiv]](https://arxiv.org/abs/1610.06918) [[Blog]](https://blog.acolyer.org/2017/02/10/learning-to-protect-communications-with-adversarial-neural-cryptography/)\n- Long Text Generation via Adversarial Training with Leaked Information [[arXiv]](https://arxiv.org/abs/1709.08624)\n- MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions [[arXiv]](https://arxiv.org/abs/1703.10847)\n- MuseGAN: Symbolic-domain Music Generation and Accompaniment with Multi-track Sequential Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1709.06298)\n- Reconstruction of three-dimensional porous media using generative adversarial neural networks [[arXiv]](https://arxiv.org/abs/1704.03225) [[Code]](https://github.com/LukasMosser/PorousMediaGan)\n- SEGAN: Speech Enhancement Generative Adversarial Network [[arXiv]](https://arxiv.org/abs/1703.09452)\n- Semi-supervised Learning of Compact Document Representations with Deep Networks [[Paper]](http://www.cs.nyu.edu/~ranzato/publications/ranzato-icml08.pdf)\n- SSGAN: Secure Steganography Based on Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1707.01613)\n- Steganographic Generative Adversarial Networks [[arXiv]](https://arxiv.org/abs/1703.05502)\n- Towards Grounding Conceptual Spaces in Neural Representations [[arXiv]](https://arxiv.org/abs/1706.04825)\n\n## Humor\n- Stopping GAN Violence: Generative Unadversarial Networks [[arXiv]](https://arxiv.org/abs/1703.02528)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnightrome%2Freally-awesome-gan","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnightrome%2Freally-awesome-gan","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnightrome%2Freally-awesome-gan/lists"}