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https://github.com/zhangqianhui/AdversarialNetsPapers

Awesome paper list with code about generative adversarial nets
https://github.com/zhangqianhui/AdversarialNetsPapers

adversarial-networks deep-learning gan image-translation

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Awesome paper list with code about generative adversarial nets

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README 

        
# AdversarialNetsPapers

A collection of resources and papers on Generation Adversarial Networks.



## Table of Contents

- [First Paper](#First-Paper)

- [Application]

  - [Image Translation](#Image-Translation)

  - [Facial Attribute Manipulation](#Facial-Attribute-Manipulation)

  - [Face Swap and Reenactment](#Facial-Attribute-Manipulation)

  - [Gaze Correction and Redirection](#Gaze-Correction-and-Redirection)

  - [Person Image Synthesis](#Facial-Attribute-Manipulation)

  - [Image Inpainting](#Image-Inpainting)

  - [Scene Generation](#Scene-Generation)

  - [Image blending](#Image-blending)

  - [Re-identification](#Re-identification)

  - [Super-Resolution](#Super-Resolution)

  - [De-Occlusion](#De-Occlusion)

  - [Semantic-Segmentation](#Semantic-Segmentation)

  - [Object-Detection](#Object-Detection)

  - [Landmark-Detection](#Landmark-Detection)

  - [Video-Prediction-and-Generation](#Video-Prediction-and-Generation)

  - [Shadow Detection and Removal](#Shadow-Detection-and-Removal)

  - [Makeup](#Makeup)

  - [3D](#3D)

  - [Improving Classification And Recong](#Improving-Classification-And-Recong)

- [Theory]

  - [Generative Models](#Generative-Models)  

  - [GAN Theory](#GAN-Theory)

- [Machine Learning]

  - [Conditional-Adversarial](#Conditional-Adversarial)

  - [Semi-Supervised Learning](#Semi-Supervised-Learning)

  - [Ensemble](#Ensemble)

- [Others]

  - [AutoML](#AutoML)

  - [Reinforcement learning](#Reinforcement-learning)

  - [Discrete Distributions](#Discrete-Distributions)

  - [RNN](#RNN)

- [Interdisciplinary]

  - [Medicine](#Medicine)

  - [MUSIC](#MUSIC)

- [Tutorial]

  - [Project](#Project)

  - [Blogs](#Blogs)

  - [Tutorial](#Tutorial)



## First paper



:heavy_check_mark: [Generative Adversarial Nets]

- [[Paper]](https://arxiv.org/abs/1406.2661)[[Code]](https://github.com/goodfeli/adversarial)(NIPS 2014)



## Image Translation



:heavy_check_mark: [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] 

- [[Paper]](https://arxiv.org/abs/1611.02200)[[Code]](https://github.com/yunjey/domain-transfer-network)



:heavy_check_mark: [Image-to-image translation using conditional adversarial nets] 

- [[Paper]](https://arxiv.org/pdf/1611.07004v1.pdf)[[Code]](https://github.com/phillipi/pix2pix)[[Code]](https://github.com/yenchenlin/pix2pix-tensorflow)



:heavy_check_mark: [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1703.05192)[[Code]](https://github.com/carpedm20/DiscoGAN-pytorch)



:heavy_check_mark: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] 

- [[Paper]](https://junyanz.github.io/CycleGAN/)[[Code]](https://github.com/junyanz/CycleGAN)



:heavy_check_mark: [CoGAN: Coupled Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1606.07536)[[Code]](https://github.com/andrewliao11/CoGAN-tensorflow)(NIPS 2016)



:heavy_check_mark: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/pdf/1701.02676.pdf)(NIPS 2017)



:heavy_check_mark: [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation] 

- [[Paper]](https://arxiv.org/abs/1704.02510)(NIPS 2017)[[Code]](https://github.com/duxingren14/DualGAN)



:heavy_check_mark: [Unsupervised Image-to-Image Translation Networks] 

- [[Paper]](https://arxiv.org/abs/1703.00848)



:heavy_check_mark: [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs] 

- [[Paper]](https://arxiv.org/abs/1711.11585)[[code]](https://github.com/NVIDIA/pix2pixHD)



:heavy_check_mark: [XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings] 

- [[Paper]](https://arxiv.org/abs/1711.05139)



:heavy_check_mark: [UNIT: UNsupervised Image-to-image Translation Networks] 

- [[Paper]](https://arxiv.org/abs/1703.00848)[[Code]](https://github.com/mingyuliutw/UNIT)(NIPS 2017)



:heavy_check_mark: [Toward Multimodal Image-to-Image Translation] 

- [[Paper]](https://arxiv.org/abs/1711.11586)[[Code]](https://github.com/junyanz/BicycleGAN)(NIPS 2017)



:heavy_check_mark: [Multimodal Unsupervised Image-to-Image Translation] 

- [[Paper]](https://arxiv.org/abs/1804.04732)[[Code]](https://github.com/nvlabs/MUNIt)



:heavy_check_mark: [Video-to-Video Synthesis] 

- [[Paper]](https://tcwang0509.github.io/vid2vid/)[[Code]](https://github.com/NVIDIA/vid2vid)



:heavy_check_mark: [Everybody Dance Now] 

- [[Paper]](https://arxiv.org/abs/1808.07371)[[Code]](https://github.com/nyoki-mtl/pytorch-EverybodyDanceNow)



:heavy_check_mark: [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation] 

- [[Paper]](https://arxiv.org/abs/1811.10666)(CVPR 2019)



:heavy_check_mark: [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation] 

- [[Paper]](https://arxiv.org/abs/1904.06807)[[Code]](https://github.com/Ha0Tang/SelectionGAN)(CVPR 2019 oral)



:heavy_check_mark: [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation] 

- [[Paper]](https://arxiv.org/pdf/1912.12215.pdf)[[Code]](https://github.com/Ha0Tang/LGGAN)(CVPR 2020)



:heavy_check_mark: [StarGAN v2: Diverse Image Synthesis for Multiple Domains] 

- [[Paper]](https://arxiv.org/pdf/1912.01865.pdf)[[Code]](https://github.com/clovaai/stargan-v2)(CVPR 2020)



:heavy_check_mark: [Structural-analogy from a Single Image Pair] 

- [[Paper]](https://arxiv.org/pdf/2004.02222v1.pdf)[[Code]](https://github.com/rmokady/structural-analogy)



:heavy_check_mark: [High-Resolution Daytime Translation Without Domain Labels] 

- [[Paper]](https://arxiv.org/abs/2003.08791)[[Code]](https://github.com/saic-mdal/HiDT)



:heavy_check_mark: [Rethinking the Truly Unsupervised Image-to-Image Translation] 

- [[Paper]](https://arxiv.org/abs/2006.06500)[[Code]](https://github.com/clovaai/tunit)



:heavy_check_mark: [Diverse Image Generation via Self-Conditioned GANs] 

- [[Paper]](http://selfcondgan.csail.mit.edu/preprint.pdf)[[Code]](https://github.com/stevliu/self-conditioned-gan)(CVPR2020)



:heavy_check_mark: [Contrastive Learning for Unpaired Image-to-Image Translation] 

- [[Paper]](http://taesung.me/ContrastiveUnpairedTranslation/)[[Code]](https://github.com/taesungp/contrastive-unpaired-translation)(ECCV2020)



## Facial Attribute Manipulation



:heavy_check_mark: [Autoencoding beyond pixels using a learned similarity metric] 

- [[Paper]](https://arxiv.org/abs/1512.09300)[[code]](https://github.com/andersbll/autoencoding_beyond_pixels)[[Tensorflow code]](https://github.com/zhangqianhui/vae-gan-tensorflow)(ICML 2016）



:heavy_check_mark: [Coupled Generative Adversarial Networks] 

- [[Paper]](http://mingyuliu.net/)[[Caffe Code]](https://github.com/mingyuliutw/CoGAN)[[Tensorflow Code]](https://github.com/andrewliao11/CoGAN-tensorflow)(NIPS 2016）



:heavy_check_mark: [Invertible Conditional GANs for image editing] 

- [[Paper]](https://drive.google.com/file/d/0B48XS5sLi1OlRkRIbkZWUmdoQmM/view)[[Code]](https://github.com/Guim3/IcGAN)(Arxiv 2016)



:heavy_check_mark: [Learning Residual Images for Face Attribute Manipulation] 

- [[Paper]](https://arxiv.org/abs/1612.05363)[[code]](https://github.com/Zhongdao/FaceAttributeManipulation)(CVPR 2017)



:heavy_check_mark: [Neural Photo Editing with Introspective Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1609.07093)[[Code]](https://github.com/ajbrock/Neural-Photo-Editor)(ICLR 2017)



:heavy_check_mark: [Neural Face Editing with Intrinsic Image Disentangling] 

- [[Paper]](https://arxiv.org/abs/1704.04131)(CVPR 2017)



:heavy_check_mark: [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] 

- [[Paper]](https://arxiv.org/abs/1705.04932)[[code]](https://github.com/Prinsphield/GeneGAN)(BMVC 2017)



:heavy_check_mark: [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] 

- [[Paper]](https://arxiv.org/abs/1704.04086)(ICCV 2017)



:heavy_check_mark: [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation] 

- [[Paper]](https://arxiv.org/abs/1711.09020)[[code]](https://github.com/yunjey/StarGAN)(CVPR 2018)



:heavy_check_mark: [Arbitrary Facial Attribute Editing: Only Change What You Want] 

- [[Paper]](https://arxiv.org/abs/1711.10678)[[code]](https://github.com/LynnHo/AttGAN-Tensorflow)(TIP 2019)



:heavy_check_mark: [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes] 

- [[Paper]](https://arxiv.org/abs/1803.10562)[[code]](https://github.com/Prinsphield/ELEGANT)(ECCV 2018)



:heavy_check_mark: [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation] 

- [[Paper]](https://arxiv.org/abs/1805.07509)[[code]](https://github.com/zhangqianhui/Sparsely-Grouped-GAN)(ACM MM2018 oral)



:heavy_check_mark: [GANimation: Anatomically-aware Facial Animation from a Single Image] 

- [[Paper]](http://www.albertpumarola.com/research/GANimation/index.html)[[code]](https://github.com/albertpumarola/GANimation)(ECCV 2018 oral)



:heavy_check_mark: [Geometry Guided Adversarial Facial Expression Synthesis] 

- [[Paper]](https://arxiv.org/abs/1712.03474)(ACM MM2018)



:heavy_check_mark: [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing] 

- [[Paper]](https://arxiv.org/abs/1904.09709)[[code]](https://github.com/csmliu/STGAN)(CVPR 2019)



:heavy_check_mark: [3d guided fine-grained face manipulation] [[Paper]](https://arxiv.org/abs/1902.08900)(CVPR 2019)



:heavy_check_mark: [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color] 

- [[Paper]](https://arxiv.org/abs/1902.06838)[[code]](https://github.com/run-youngjoo/SC-FEGAN)(ICCV 2019)



:heavy_check_mark: [A Survey of Deep Facial Attribute Analysis] 

- [[Paper]](https://link.springer.com/content/pdf/10.1007/s11263-020-01308-z.pdf)(IJCV 2019)



:heavy_check_mark: [PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing] 

- [[Paper]](https://arxiv.org/abs/2007.05892)[[code]](https://github.com/LynnHo/PA-GAN-Tensorflow)（Arxiv 2020）



:heavy_check_mark: [SSCGAN: Facial Attribute Editing via StyleSkip Connections] 

- [[Paper]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123600409.pdf)(ECCV 2020)



:heavy_check_mark: [CAFE-GAN: Arbitrary Face Attribute Editingwith Complementary Attention Feature] 

- [[Paper]](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590511.pdf)(ECCV 2020)



## Generative Models



:heavy_check_mark: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1511.06434)[[Code]](https://github.com/jacobgil/keras-dcgan)(Gan with convolutional networks)(ICLR 2015)



:heavy_check_mark: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1506.05751)[[Code]](https://github.com/AaronYALai/Generative_Adversarial_Networks_PyTorch/tree/master/LAPGAN)(NIPS 2015)



:heavy_check_mark: [Generative Adversarial Text to Image Synthesis] 

- [[Paper]](https://arxiv.org/abs/1605.05396)[[Code]](https://github.com/reedscot/icml2016)[[code]](https://github.com/paarthneekhara/text-to-image)



:heavy_check_mark: [Improved Techniques for Training GANs] 

- [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper)



:heavy_check_mark: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] 

- [[Paper]](https://arxiv.org/abs/1612.00005v1)[[Code]](https://github.com/Evolving-AI-Lab/ppgn)



:heavy_check_mark: [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/pdf/1612.03242v1.pdf)[[Code]](https://github.com/hanzhanggit/StackGAN)



:heavy_check_mark: [Improved Training of Wasserstein GANs] 

- [[Paper]](https://arxiv.org/abs/1704.00028)[[Code]](https://github.com/igul222/improved_wgan_training)



:heavy_check_mark: [Boundary Equibilibrium Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1703.10717)[[Code]](https://github.com/artcg/BEGAN)



:heavy_check_mark: [Progressive Growing of GANs for Improved Quality, Stability, and Variation] 

- [[Paper]](http://research.nvidia.com/publication/2017-10_Progressive-Growing-of)[[Code]](https://github.com/tkarras/progressive_growing_of_gans)[[Tensorflow Code]](https://github.com/zhangqianhui/PGGAN-tensorflow)



:heavy_check_mark: [ Self-Attention Generative Adversarial Networks ] 

- [[Paper]](https://arxiv.org/abs/1805.08318)[[Code]](https://github.com/heykeetae/Self-Attention-GAN)(NIPS 2018)



:heavy_check_mark: [Large Scale GAN Training for High Fidelity Natural Image Synthesis] 

- [[Paper]](https://arxiv.org/abs/1809.11096)(ICLR 2019)



:heavy_check_mark: [A Style-Based Generator Architecture for Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/pdf/1812.04948)[[Code]](https://github.com/NVlabs/stylegan)



:heavy_check_mark: [Analyzing and Improving the Image Quality of StyleGAN] 

- [[Paper]](http://arxiv.org/abs/1912.04958)[[Code]](https://github.com/NVlabs/stylegan2)



:heavy_check_mark: [SinGAN: Learning a Generative Model from a Single Natural Image] 

- [[Paper]](https://arxiv.org/pdf/1905.01164.pdf)[[Code]](https://github.com/tamarott/SinGAN)(ICCV2019 best paper)



:heavy_check_mark: [Real or Not Real, that is the Question] 

- [[Paper]](https://openreview.net/forum?id=B1lPaCNtPB)[[Code]](https://github.com/kam1107/RealnessGAN)(ICLR2020 Spot)



:heavy_check_mark: [Training End-to-end Single Image Generators without GANs] 

- [[Paper]](https://arxiv.org/pdf/2004.06014.pdf)



:heavy_check_mark: [Adversarial Latent Autoencoders] 

- [[Paper]](https://arxiv.org/abs/2004.04467)[[code]](https://github.com/podgorskiy/ALAE)



## Gaze Correction and Redirection



:heavy_check_mark: [DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation] 

- [[Paper]](https://arxiv.org/abs/1607.07215)[[code]](https://github.com/BlueWinters/DeepWarp)(ECCV 2016)



:heavy_check_mark: [Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1903.12530)[[Code]](https://github.com/HzDmS/gaze_redirection)(ICCV 2019)



:heavy_check_mark: [GazeCorrection:Self-Guided Eye Manipulation in the wild using Self-Supervised Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1906.00805)[[code]](https://github.com/zhangqianhui/GazeCorrection)



:heavy_check_mark: [MGGR: MultiModal-Guided Gaze Redirection with Coarse-to-Fine Learning] 

- [[Paper]](https://arxiv.org/pdf/2004.03064.pdf)



:heavy_check_mark: [Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild] 

- [[Paper]](https://arxiv.org/abs/2008.03834)[[Code]](https://github.com/zhangqianhui/GazeAnimation)(ACM MM2020)



## AutoML



:heavy_check_mark: [AutoGAN: Neural Architecture Search for Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1908.03835)[[Code]](https://github.com/TAMU-VITA/AutoGAN)(ICCV 2019)



## Image Animation



:heavy_check_mark: [Animating arbitrary objects via deep motion transfer] 

- [[Paper]](https://arxiv.org/abs/1812.08861)[[code]](https://github.com/AliaksandrSiarohin/monkey-net)(CVPR 2019)



:heavy_check_mark: [First Order Motion Model for Image Animation] 

- [[Paper]](https://arxiv.org/abs/2003.00196)[[code]](https://github.com/AliaksandrSiarohin/first-order-model)(NIPS 2019)



## GAN Theory



:heavy_check_mark: [Energy-based generative adversarial network] 

- [[Paper]](https://arxiv.org/pdf/1609.03126v2.pdf)[[Code]](https://github.com/buriburisuri/ebgan)(Lecun paper)



:heavy_check_mark: [Improved Techniques for Training GANs] 

- [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper)



:heavy_check_mark: [Mode Regularized Generative Adversarial Networks] 

- [[Paper]](https://openreview.net/pdf?id=HJKkY35le)(Yoshua Bengio , ICLR 2017)



:heavy_check_mark: [Improving Generative Adversarial Networks with Denoising Feature Matching] 

- [[Paper]](https://openreview.net/pdf?id=S1X7nhsxl)[[Code]](https://github.com/hvy/chainer-gan-denoising-feature-matching)(Yoshua Bengio , ICLR 2017)



:heavy_check_mark: [Sampling Generative Networks] 

- [[Paper]](https://arxiv.org/abs/1609.04468)[[Code]](https://github.com/dribnet/plat)



:heavy_check_mark: [How to train Gans] 

- [[Docu]](https://github.com/soumith/ganhacks#authors)



:heavy_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] 

- [[Paper]](http://openreview.net/forum?id=Hk4_qw5xe)(ICLR 2017)



:heavy_check_mark: [Unrolled Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1611.02163)[[Code]](https://github.com/poolio/unrolled_gan)(ICLR 2017)



:heavy_check_mark: [Least Squares Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1611.04076)[[Code]](https://github.com/pfnet-research/chainer-LSGAN)(ICCV 2017)



:heavy_check_mark: [Wasserstein GAN] 

- [[Paper]](https://arxiv.org/abs/1701.07875)[[Code]](https://github.com/martinarjovsky/WassersteinGAN)



:heavy_check_mark: [Improved Training of Wasserstein GANs] 

- [[Paper]](https://arxiv.org/abs/1704.00028)[[Code]](https://github.com/igul222/improved_wgan_training)(The improve of wgan)



:heavy_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1701.04862)



:heavy_check_mark: [Generalization and Equilibrium in Generative Adversarial Nets] 

- [[Paper]](https://arxiv.org/abs/1703.00573)（ICML 2017）



:heavy_check_mark: [GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium]

- [[Paper]](https://arxiv.org/abs/1706.08500)[[code]](https://github.com/bioinf-jku/TTUR)



:heavy_check_mark: [Spectral Normalization for Generative Adversarial Networks]

- [[Paper]](https://openreview.net/forum?id=B1QRgziT-)[[code]](https://github.com/minhnhat93/tf-SNDCGAN)（ICLR 2018）



:heavy_check_mark: [Which Training Methods for GANs do actually Converge]

- [[Paper]](https://arxiv.org/pdf/1801.04406.pdf)[[code]](https://github.com/LMescheder/GAN_stability)（ICML 2018）



:heavy_check_mark: [Self-Supervised Generative Adversarial Networks]

- [[Paper]](https://arxiv.org/abs/1811.11212)[[code]](https://github.com/google/compare_gan)（CVPR 2019）



## Image Inpainting



:heavy_check_mark: [Semantic Image Inpainting with Perceptual and Contextual Losses] 

- [[Paper]](https://arxiv.org/abs/1607.07539)[[Code]](https://github.com/bamos/dcgan-completion.tensorflow)(CVPR 2017)



:heavy_check_mark: [Context Encoders: Feature Learning by Inpainting] 

- [[Paper]](https://arxiv.org/abs/1604.07379)[[Code]](https://github.com/jazzsaxmafia/Inpainting)



:heavy_check_mark: [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1611.06430v1)



:heavy_check_mark: [Generative face completion] 

- [[Paper]](https://drive.google.com/file/d/0B8_MZ8a8aoSeenVrYkpCdnFRVms/edit)[[code]](https://github.com/Yijunmaverick/GenerativeFaceCompletion)(CVPR2017)



:heavy_check_mark: [Globally and Locally Consistent Image Completion] 

- [[MainPAGE]](http://hi.cs.waseda.ac.jp/~iizuka/projects/completion/en/)[[code]](https://github.com/satoshiiizuka/siggraph2017_inpainting)(SIGGRAPH 2017)



:heavy_check_mark: [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis] 

- [[Paper]](https://arxiv.org/abs/1611.09969)[[code]](https://github.com/leehomyc/Faster-High-Res-Neural-Inpainting)(CVPR 2017)



:heavy_check_mark: [Eye In-Painting with Exemplar Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1712.03999)[[Introduction]](https://github.com/bdol/exemplar_gans)[[Tensorflow code]](https://github.com/zhangqianhui/Exemplar_GAN_Eye_Inpainting)(CVPR2018)



:heavy_check_mark: [Generative Image Inpainting with Contextual Attention] 

- [[Paper]](https://arxiv.org/abs/1801.07892)[[Project]](http://jiahuiyu.com/deepfill)[[Demo]](http://jiahuiyu.com/deepfill)[[YouTube]](https://youtu.be/xz1ZvcdhgQ0)[[Code]](https://github.com/JiahuiYu/generative_inpainting)(CVPR2018)



:heavy_check_mark: [Free-Form Image Inpainting with Gated Convolution] 

- [[Paper]](https://arxiv.org/abs/1806.03589)[[Project]](http://jiahuiyu.com/deepfill2)[[YouTube]](https://youtu.be/uZkEi9Y2dj4)



:heavy_check_mark: [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning] 

- [[Paper]](https://arxiv.org/abs/1901.00212)[[Code]](https://github.com/knazeri/edge-connect)



## Scene Generation



:heavy_check_mark: [a layer-based sequential framework for scene generation with gans] 

- [[Paper]](https://arxiv.org/abs/1902.00671)[[Code]](https://github.com/0zgur0/Seq_Scene_Gen)(AAAI 2019)



## Semi-Supervised Learning



:heavy_check_mark: [Adversarial Training Methods for Semi-Supervised Text Classification] 

- [[Paper]](https://arxiv.org/abs/1605.07725)[[Note]](https://github.com/dennybritz/deeplearning-papernotes/blob/master/notes/adversarial-text-classification.md)( Ian Goodfellow Paper)



:heavy_check_mark: [Improved Techniques for Training GANs] 

- [[Paper]](https://arxiv.org/abs/1606.03498)[[Code]](https://github.com/openai/improved-gan)(Goodfellow's paper)



:heavy_check_mark: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1511.06390)(ICLR)



:heavy_check_mark: [Semi-Supervised QA with Generative Domain-Adaptive Nets] 

- [[Paper]](https://arxiv.org/abs/1702.02206)(ACL 2017)



:heavy_check_mark: [Good Semi-supervised Learning that Requires a Bad GAN] 

- [[Paper]](https://arxiv.org/abs/1705.09783)[[Code]](https://github.com/kimiyoung/ssl_bad_gan)(NIPS 2017)



## Ensemble



:heavy_check_mark: [AdaGAN: Boosting Generative Models] 

- [[Paper]](https://arxiv.org/abs/1701.02386)[[Code]]（Google Brain）



## Image blending



:heavy_check_mark: [GP-GAN: Towards Realistic High-Resolution Image Blending] 

- [[Paper]](https://arxiv.org/abs/1703.07195)[[Code]](https://github.com/wuhuikai/GP-GAN)



## Re-identification



:heavy_check_mark: [Joint Discriminative and Generative Learning for Person Re-identification] 

- [[Paper]](https://arxiv.org/abs/1904.07223)[[Code]](https://github.com/NVlabs/DG-Net)[[YouTube]](https://www.youtube.com/watch?v=ubCrEAIpQs4) [[Bilibili]](https://www.bilibili.com/video/av51439240) (CVPR2019 Oral)



:heavy_check_mark: [Pose-Normalized Image Generation for Person Re-identification] 

- [[Paper]](https://arxiv.org/abs/1712.02225)[[Code]](https://github.com/naiq/PN_GAN)(ECCV 2018)



## Super-Resolution



:heavy_check_mark: [Image super-resolution through deep learning]

- [[Code]](https://github.com/david-gpu/srez)(Just for face dataset)



:heavy_check_mark: [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] 

- [[Paper]](https://arxiv.org/abs/1609.04802)[[Code]](https://github.com/leehomyc/Photo-Realistic-Super-Resoluton)（Using Deep residual network）



:heavy_check_mark: [EnhanceGAN] 

- [[Docs]](https://medium.com/@richardherbert/faces-from-noise-super-enhancing-8x8-images-with-enhancegan-ebda015bb5e0#.io6pskvin)[[Code]]



:heavy_check_mark: [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks]

- [[Paper]](https://arxiv.org/abs/1809.00219)[[Code]](https://github.com/xinntao/ESRGAN)(ECCV 2018 workshop)



## De-Occlusion



:heavy_check_mark: [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] 

- [[Paper]](https://arxiv.org/abs/1612.08534)



## Semantic Segmentation



:heavy_check_mark: [Adversarial Deep Structural Networks for Mammographic Mass Segmentation] 

- [[Paper]](https://arxiv.org/abs/1612.05970)[[Code]](https://github.com/wentaozhu/adversarial-deep-structural-networks)



:heavy_check_mark: [Semantic Segmentation using Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1611.08408)（soumith's paper）



## Object Detection



:heavy_check_mark: [Perceptual generative adversarial networks for small object detection] 

- [[Paper]](https://arxiv.org/abs/1706.05274v2)(CVPR 2017)



:heavy_check_mark: [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] 

- [[Paper]](http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf)[[code]](https://github.com/xiaolonw/adversarial-frcnn)(CVPR2017)



## Landmark Detection



:heavy_check_mark: [Style aggregated network for facial landmark detection] 

- [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Dong_Style_Aggregated_Network_CVPR_2018_paper.pdf)(CVPR 2018)



## Conditional Adversarial



:heavy_check_mark: [Conditional Generative Adversarial Nets] 

- [[Paper]](https://arxiv.org/abs/1411.1784)[[Code]](https://github.com/zhangqianhui/Conditional-Gans)



:heavy_check_mark: [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] 

- [[Paper]](https://arxiv.org/abs/1606.03657)[[Code]](https://github.com/buriburisuri/supervised_infogan)[[Code]](https://github.com/openai/InfoGAN)



:heavy_check_mark: [Conditional Image Synthesis With Auxiliary Classifier GANs] 

- [[Paper]](https://arxiv.org/abs/1610.09585)[[Code]](https://github.com/buriburisuri/ac-gan)(GoogleBrain ICLR 2017)



:heavy_check_mark: [Pixel-Level Domain Transfer] 

- [[Paper]](https://arxiv.org/pdf/1603.07442v2.pdf)[[Code]](https://github.com/fxia22/pldtgan)



:heavy_check_mark: [Invertible Conditional GANs for image editing] 

- [[Paper]](https://arxiv.org/abs/1611.06355)[[Code]](https://github.com/Guim3/IcGAN)



:heavy_check_mark: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] 

- [[Paper]](https://arxiv.org/abs/1612.00005v1)[[Code]](https://github.com/Evolving-AI-Lab/ppgn)



:heavy_check_mark: [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/pdf/1612.03242v1.pdf)[[Code]](https://github.com/hanzhanggit/StackGAN)



## Video Prediction and Generation



:heavy_check_mark: [Deep multi-scale video prediction beyond mean square error] 

- [[Paper]](https://arxiv.org/abs/1511.05440)[[Code]](https://github.com/dyelax/Adversarial_Video_Generation)(Yann LeCun's paper)



:heavy_check_mark: [Generating Videos with Scene Dynamics] 

- [[Paper]](https://arxiv.org/abs/1609.02612)[[Web]](http://web.mit.edu/vondrick/tinyvideo/)[[Code]](https://github.com/cvondrick/videogan)



:heavy_check_mark: [MoCoGAN: Decomposing Motion and Content for Video Generation] 

- [[Paper]](https://arxiv.org/abs/1707.04993)



## Shadow Detection and Removal



:heavy_check_mark: [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal] 

- [[Paper]](https://arxiv.org/abs/1908.01323)[[Code]](https://github.com/TAMU-VITA/ShapeMatchingGAN)(ICCV 2019)



## Makeup



:heavy_check_mark: [BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network] 

- [[Paper]](https://dl.acm.org/citation.cfm?id=3240618)(ACMMM 2018)



## Reinforcement learning



:heavy_check_mark: [Connecting Generative Adversarial Networks and Actor-Critic Methods] 

- [[Paper]](https://arxiv.org/abs/1610.01945)(NIPS 2016 workshop)



## RNN



:heavy_check_mark: [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] 

- [[Paper]](https://arxiv.org/abs/1611.09904)[[Code]](https://github.com/olofmogren/c-rnn-gan)



:heavy_check_mark: [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient] 

- [[Paper]](https://arxiv.org/abs/1609.05473)[[Code]](https://github.com/LantaoYu/SeqGAN)(AAAI 2017)



# Medicine



:heavy_check_mark: [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery] 

- [[Paper]](https://arxiv.org/abs/1703.05921)



## 3D



:heavy_check_mark: [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] 

- [[Paper]](https://arxiv.org/abs/1610.07584)[[Web]](http://3dgan.csail.mit.edu/)[[code]](https://github.com/zck119/3dgan-release)(2016 NIPS)



:heavy_check_mark: [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] 

- [[Web]](http://www.cs.unc.edu/%7Eeunbyung/tvsn/)(CVPR 2017)



## MUSIC



:heavy_check_mark: [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] 

- [[Paper]](https://arxiv.org/abs/1703.10847)[[HOMEPAGE]](https://richardyang40148.github.io/TheBlog/midinet_arxiv_demo.html)



## Discrete distributions



:heavy_check_mark: [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1702.07983v1)



:heavy_check_mark: [Boundary-Seeking Generative Adversarial Networks] 

- [[Paper]](https://arxiv.org/abs/1702.08431)



:heavy_check_mark: [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] 

- [[Paper]](https://arxiv.org/abs/1611.04051)



## Improving Classification And Recong



:heavy_check_mark: [Generative OpenMax for Multi-Class Open Set Classification] 

- [[Paper]](https://arxiv.org/pdf/1707.07418.pdf)(BMVC 2017)



:heavy_check_mark: [Controllable Invariance through Adversarial Feature Learning] 

- [[Paper]](https://arxiv.org/abs/1705.11122)[[code]](https://github.com/github-pengge/adversarial_invariance_feature_learning)(NIPS 2017)



:heavy_check_mark: [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] 

- [[Paper]](https://arxiv.org/abs/1701.07717)[[Code]](https://github.com/layumi/Person-reID_GAN) (ICCV2017)



:heavy_check_mark: [Learning from Simulated and Unsupervised Images through Adversarial Training] 

- [[Paper]](https://arxiv.org/abs/1612.07828)[[code]](https://github.com/carpedm20/simulated-unsupervised-tensorflow)（Apple paper, CVPR 2017 Best Paper）



:heavy_check_mark: [GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification] 

- [[Paper]](https://www.sciencedirect.com/science/article/pii/S0925231218310749) (Neurocomputing Journal (2018), Elsevier）



# Project



:heavy_check_mark: [cleverhans] 

- [[Code]](https://github.com/openai/cleverhans)(A library for benchmarking vulnerability to adversarial examples)



:heavy_check_mark: [reset-cppn-gan-tensorflow] 

- [[Code]](https://github.com/hardmaru/resnet-cppn-gan-tensorflow)(Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images)



:heavy_check_mark: [HyperGAN] 

- [[Code]](https://github.com/255bits/HyperGAN)(Open source GAN focused on scale and usability)



# Blogs

| Author | Address |

|:----:|:---:|

| **inFERENCe** |  [Adversarial network](http://www.inference.vc/)  |

| **inFERENCe** |  [InfoGan](http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/)  |

| **distill** |  [Deconvolution and Image Generation](http://distill.pub/2016/deconv-checkerboard/)  |

| **yingzhenli** |  [Gan theory](http://www.yingzhenli.net/home/blog/?p=421http://www.yingzhenli.net/home/blog/?p=421)  |

| **OpenAI** |  [Generative model](https://openai.com/blog/generative-models/)  |



# Tutorial



:heavy_check_mark: [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[[Chinese Trans]](http://c.m.163.com/news/a/C7UE2MLT0511AQHO.html?spss=newsapp&spsw=1)[[details]](https://arxiv.org/pdf/1701.00160v1.pdf)



:heavy_check_mark: [2] [[PDF]](https://drive.google.com/file/d/0BxKBnD5y2M8NbzBUbXRwUDBZOVU/view)(NIPS Lecun Slides)



:heavy_check_mark: [3] [[ICCV 2017 Tutorial About GANS]](https://sites.google.com/view/iccv-2017-gans/schedule)



:heavy_check_mark: [3] [[A Mathematical Introduction to Generative Adversarial Nets (GAN)]](https://arxiv.org/abs/2009.00169)