The classic about Generative Adversarial Networks
First paper
[Generative Adversarial Nets] [Paper] [Code](the First paper of GAN)
Unclassified
[Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [Paper][Code]
[Adversarial Autoencoders] [Paper][Code]
[Generating Images with Perceptual Similarity Metrics based on Deep Networks] [Paper]
[Generating images with recurrent adversarial networks] [Paper][Code]
[Generative Visual Manipulation on the Natural Image Manifold] [Paper][Code]
[Learning What and Where to Draw] [Paper][Code]
[Adversarial Training for Sketch Retrieval] [Paper]
[Generative Image Modeling using Style and Structure Adversarial Networks] [Paper][Code]
[Generative Adversarial Networks as Variational Training of Energy Based Models] [Paper](ICLR 2017)
[Synthesizing the preferred inputs for neurons in neural networks via deep generator networks] [Paper][Code]
[SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [Paper][Code]
[Adversarial Feature Learning] [Paper]
[Adversarially Learned Inference][Paper][Code]
GAN Theory
[Energy-based generative adversarial network] [Paper][Code](Lecun paper)
[Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)
[Mode Regularized Generative Adversarial Networks] [Paper](Yoshua Bengio , ICLR 2017)
[Improving Generative Adversarial Networks with Denoising Feature Matching] [Paper][Code](Yoshua Bengio , ICLR 2017)
[Sampling Generative Networks] [Paper][Code]
[How to train Gans] [Docu]
[Towards Principled Methods for Training Generative Adversarial Networks] [Paper](ICLR 2017)
[Unrolled Generative Adversarial Networks] [Paper][Code](ICLR 2017)
[Least Squares Generative Adversarial Networks] [Paper][Code](ICCV 2017)
[Wasserstein GAN] [Paper][Code]
[Improved Training of Wasserstein GANs] [Paper][Code](The improve of wgan)
[Towards Principled Methods for Training Generative Adversarial Networks] [Paper]
[Generalization and Equilibrium in Generative Adversarial Nets] [Paper]（ICML 2017）
[GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium][Paper][code]
[Spectral Normalization for Generative Adversarial Networks][Paper][code]（ICLR 2018）
[Which Training Methods for GANs do actually Converge][Paper][code]（ICML 2018）
Generation High-Quality Images
[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)(ICLR)
[Generative Adversarial Text to Image Synthesis] [Paper][Code][code]
[Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)
[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [Paper][Code]
[StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [Paper][Code]
[Improved Training of Wasserstein GANs] [Paper][Code]
[Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow] [Paper][Code]
[Progressive Growing of GANs for Improved Quality, Stability, and Variation] [Paper][Code][Tensorflow Code]
[ Self-Attention Generative Adversarial Networks ] [Paper][Code]
Semi-supervised learning
[Adversarial Training Methods for Semi-Supervised Text Classification] [Paper][Note]( Ian Goodfellow Paper)
[Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)
[Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper](ICLR)
[Semi-Supervised QA with Generative Domain-Adaptive Nets] [Paper](ACL 2017)
[Good Semi-supervised Learning that Requires a Bad GAN] [Paper][Code](NIPS 2017)
Ensemble
[AdaGAN: Boosting Generative Models] [Paper][[Code]]（Google Brain）
Image blending
[GP-GAN: Towards Realistic High-Resolution Image Blending] [Paper][Code]
Image Inpainting
[Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code](CVPR 2017)
[Context Encoders: Feature Learning by Inpainting] [Paper][Code]
[Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper]
[Generative face completion] [Paper][code](CVPR2017)
[Globally and Locally Consistent Image Completion] [MainPAGE][code](SIGGRAPH 2017)
[High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis] [Paper][code](CVPR 2017)
[Eye In-Painting with Exemplar Generative Adversarial Networks] [Paper][Introduction][Tensorflow code](CVPR2018)
[Generative Image Inpainting with Contextual Attention] [Paper][Project][Demo][YouTube][Code](CVPR2018)
[Free-Form Image Inpainting with Gated Convolution] [Paper][Project][YouTube]
Re-identification
[Pose-Normalized Image Generation for Person Re-identification] [Paper][Code](ECCV 2018)
Super-Resolution
[Image super-resolution through deep learning ][Code](Just for face dataset)
[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [Paper][Code]（Using Deep residual network）
[EnhanceGAN] [Docs][[Code]]
[ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks] [Paper][Code](ECCV 2018 workshop)
De-Occlusion
[Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [Paper]
Semantic Segmentation
[Adversarial Deep Structural Networks for Mammographic Mass Segmentation] [Paper][Code]
[Semantic Segmentation using Adversarial Networks] [Paper]（soumith's paper）
Object Detection
[Perceptual generative adversarial networks for small object detection] [Paper](CVPR 2017)
[A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [Paper][code](CVPR2017)
Conditional adversarial
[Conditional Generative Adversarial Nets] [Paper][Code]
[InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [Paper][Code][Code]
[Conditional Image Synthesis With Auxiliary Classifier GANs] [Paper][Code](GoogleBrain ICLR 2017)
[Pixel-Level Domain Transfer] [Paper][Code]
[Invertible Conditional GANs for image editing] [Paper][Code]
[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [Paper][Code]
[StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [Paper][Code]
Video Prediction and Generation
[Deep multi-scale video prediction beyond mean square error] [Paper][Code](Yann LeCun's paper)
[Generating Videos with Scene Dynamics] [Paper][Web][Code]
[MoCoGAN: Decomposing Motion and Content for Video Generation] [Paper]
Texture Synthesis & style transfer
[Precomputed real-time texture synthesis with markovian generative adversarial networks] [Paper][Code](ECCV 2016)
Image translation
[UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] [Paper][Code]
[Image-to-image translation using conditional adversarial nets] [Paper][Code][Code]
[Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [Paper][Code]
[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [Paper][Code]
[CoGAN: Coupled Generative Adversarial Networks] [Paper][Code](NIPS 2016)
[Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper](NIPS 2017)
[Unsupervised Image-to-Image Translation Networks] [Paper]
[Triangle Generative Adversarial Networks] [Paper]
[High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs] [Paper][code]
[XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings] [Paper](Reviewed)
[UNIT: UNsupervised Image-to-image Translation Networks] [Paper][Code](NIPS 2017)
[Toward Multimodal Image-to-Image Translation] [Paper][Code](NIPS 2017)
[Multimodal Unsupervised Image-to-Image Translation] [Paper][Code]
[Video-to-Video Synthesis] [Paper][Code]
Facial Attribute Manipulation
[Autoencoding beyond pixels using a learned similarity metric] [Paper][code][Tensorflow code]
[Coupled Generative Adversarial Networks] [Paper][Caffe Code][Tensorflow Code]（NIPS）
[Invertible Conditional GANs for image editing] [Paper][Code]
[Learning Residual Images for Face Attribute Manipulation] [Paper][code](CVPR 2017)
[Neural Photo Editing with Introspective Adversarial Networks] [Paper][Code](ICLR 2017)
[Neural Face Editing with Intrinsic Image Disentangling] [Paper](CVPR 2017)
[GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] [Paper][code](BMVC 2017)
[ST-GAN: Unsupervised Facial Image Semantic Transformation Using Generative Adversarial Networks] [Paper]
[Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] [Paper](ICCV 2017)
[StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation] [Paper][code](CVPR 2018)
[Arbitrary Facial Attribute Editing: Only Change What You Want] [Paper][code]
[ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes] [Paper][code](ECCV 2018)
[Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation] [Paper][code](ACM MM2018 oral)
[GANimation: Anatomically-aware Facial Animation from a Single Image] [Paper][code](ECCV 2018 oral)
Reinforcement learning
[Connecting Generative Adversarial Networks and Actor-Critic Methods] [Paper](NIPS 2016 workshop)
RNN
[C-RNN-GAN: Continuous recurrent neural networks with adversarial training] [Paper][Code] [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient] [Paper][Code](AAAI 2017)
Medicine
[Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery] [Paper]
3D
[Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [Paper][Web][code](2016 NIPS)
[Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] [Web](CVPR 2017)
MUSIC
[MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] [Paper][HOMEPAGE]
For discrete distributions
[Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [Paper]
[Boundary-Seeking Generative Adversarial Networks] [Paper]
[GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] [Paper]
Improving Classification And Recong
[Generative OpenMax for Multi-Class Open Set Classification] [Paper](BMVC 2017)
[Controllable Invariance through Adversarial Feature Learning] [Paper][code](NIPS 2017)
[Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] [Paper][Code] (ICCV2017)
[Learning from Simulated and Unsupervised Images through Adversarial Training] [Paper][code]（Apple paper, CVPR 2017 Best Paper）
Project
[cleverhans] [Code](A library for benchmarking vulnerability to adversarial examples)
[reset-cppn-gan-tensorflow] [Code](Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images)
[HyperGAN] [Code](Open source GAN focused on scale and usability)
Blogs
Tutorial
[1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]
[2] [PDF](NIPS Lecun Slides)
[3] [ICCV 2017 Tutorial About GANS]
Reference:
https://github.com/zhangqianhui/AdversarialNetsPapers