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A collection of resources and papers on Generation Adversarial Networks.

Table of Contents

First paper

✔️ [Generative Adversarial Nets]

Image Translation


✔️ [Image-to-image translation using conditional adversarial nets]

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

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

✔️ [CoGAN: Coupled Generative Adversarial Networks]

✔️ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks]

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

✔️ [Unsupervised Image-to-Image Translation Networks]

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

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

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

✔️ [Toward Multimodal Image-to-Image Translation]

✔️ [Multimodal Unsupervised Image-to-Image Translation]

✔️ [Video-to-Video Synthesis]

✔️ [Everybody Dance Now]

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

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

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

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

✔️ [Structural-analogy from a Single Image Pair]

✔️ [High-Resolution Daytime Translation Without Domain Labels]

✔️ [Rethinking the Truly Unsupervised Image-to-Image Translation]

✔️ [Diverse Image Generation via Self-Conditioned GANs]

✔️ [Contrastive Learning for Unpaired Image-to-Image Translation]

Facial Attribute Manipulation

✔️ [Autoencoding beyond pixels using a learned similarity metric]

✔️ [Coupled Generative Adversarial Networks]

✔️ [Invertible Conditional GANs for image editing]

✔️ [Learning Residual Images for Face Attribute Manipulation]

✔️ [Neural Photo Editing with Introspective Adversarial Networks]

✔️ [Neural Face Editing with Intrinsic Image Disentangling]

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

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

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

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

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

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

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

✔️ [Geometry Guided Adversarial Facial Expression Synthesis]

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

✔️ [3d guided fine-grained face manipulation] [Paper](CVPR 2019)

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

✔️ [A Survey of Deep Facial Attribute Analysis]

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

✔️ [SSCGAN: Facial Attribute Editing via StyleSkip Connections]

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

Generative Models

✔️ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks]

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

✔️ [Generative Adversarial Text to Image Synthesis]

✔️ [Improved Techniques for Training GANs]

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

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

✔️ [Improved Training of Wasserstein GANs]

✔️ [Boundary Equibilibrium Generative Adversarial Networks]

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

✔️ [ Self-Attention Generative Adversarial Networks ]

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

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

✔️ [Analyzing and Improving the Image Quality of StyleGAN]

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

✔️ [Real or Not Real, that is the Question]

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

✔️ [Adversarial Latent Autoencoders]

Gaze Correction and Redirection

✔️ [DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation]

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

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

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

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


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

Image Animation

✔️ [Animating arbitrary objects via deep motion transfer]

✔️ [First Order Motion Model for Image Animation]

GAN Theory

✔️ [Energy-based generative adversarial network]

✔️ [Improved Techniques for Training GANs]

✔️ [Mode Regularized Generative Adversarial Networks]

  • [Paper](Yoshua Bengio , ICLR 2017)

✔️ [Improving Generative Adversarial Networks with Denoising Feature Matching]

✔️ [Sampling Generative Networks]

✔️ [How to train Gans]

✔️ [Towards Principled Methods for Training Generative Adversarial Networks]

✔️ [Unrolled Generative Adversarial Networks]

✔️ [Least Squares Generative Adversarial Networks]

✔️ [Wasserstein GAN]

✔️ [Improved Training of Wasserstein GANs]

✔️ [Towards Principled Methods for Training Generative Adversarial Networks]

✔️ [Generalization and Equilibrium in Generative Adversarial Nets]

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

✔️ [Spectral Normalization for Generative Adversarial Networks]

✔️ [Which Training Methods for GANs do actually Converge]

✔️ [Self-Supervised Generative Adversarial Networks]

Image Inpainting

✔️ [Semantic Image Inpainting with Perceptual and Contextual Losses]

✔️ [Context Encoders: Feature Learning by Inpainting]

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

✔️ [Generative face completion]

✔️ [Globally and Locally Consistent Image Completion]

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

✔️ [Eye In-Painting with Exemplar Generative Adversarial Networks]

✔️ [Generative Image Inpainting with Contextual Attention]

✔️ [Free-Form Image Inpainting with Gated Convolution]

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

Scene Generation

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

Semi-Supervised Learning

✔️ [Adversarial Training Methods for Semi-Supervised Text Classification]

✔️ [Improved Techniques for Training GANs]

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

✔️ [Semi-Supervised QA with Generative Domain-Adaptive Nets]

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


✔️ [AdaGAN: Boosting Generative Models]

  • [Paper][[Code]](Google Brain)

Image blending

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


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

✔️ [Pose-Normalized Image Generation for Person Re-identification]


✔️ [Image super-resolution through deep learning]

  • [Code](Just for face dataset)

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

✔️ [EnhanceGAN]

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


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

Semantic Segmentation

✔️ [Adversarial Deep Structural Networks for Mammographic Mass Segmentation]

✔️ [Semantic Segmentation using Adversarial Networks]

Object Detection

✔️ [Perceptual generative adversarial networks for small object detection]

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

Landmark Detection

✔️ [Style aggregated network for facial landmark detection]

Conditional Adversarial

✔️ [Conditional Generative Adversarial Nets]

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

✔️ [Conditional Image Synthesis With Auxiliary Classifier GANs]

✔️ [Pixel-Level Domain Transfer]

✔️ [Invertible Conditional GANs for image editing]

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

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

Video Prediction and Generation

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

✔️ [Generating Videos with Scene Dynamics]

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

Shadow Detection and Removal

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


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

Reinforcement learning

✔️ [Connecting Generative Adversarial Networks and Actor-Critic Methods]


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

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


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


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

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


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

Discrete distributions

✔️ [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks]

✔️ [Boundary-Seeking Generative Adversarial Networks]

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

Improving Classification And Recong

✔️ [Generative OpenMax for Multi-Class Open Set Classification]

✔️ [Controllable Invariance through Adversarial Feature Learning]

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

✔️ [Learning from Simulated and Unsupervised Images through Adversarial Training]

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

  • [Paper] (Neurocomputing Journal (2018), Elsevier)


✔️ [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)


Author Address
inFERENCe Adversarial network
inFERENCe InfoGan
distill Deconvolution and Image Generation
yingzhenli Gan theory
OpenAI Generative model


✔️ [1] (NIPS Goodfellow Slides)[Chinese Trans][details]

✔️ [2] [PDF](NIPS Lecun Slides)

✔️ [3] [ICCV 2017 Tutorial About GANS]

✔️ [3] [A Mathematical Introduction to Generative Adversarial Nets (GAN)]

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