Table of Contents

• First Paper
• [Application]
  • Image Translation
  • Facial Attribute Manipulation
  • Face Swap and Reenactment
  • Gaze Correction and Redirection
  • Person Image Synthesis
  • Image Inpainting
  • Scene Generation
  • Image blending
  • Re-identification
  • Super-Resolution
  • De-Occlusion
  • Semantic-Segmentation
  • Object-Detection
  • Landmark-Detection
  • Video-Prediction-and-Generation
  • Shadow Detection and Removal
  • Makeup
  • 3D
  • Improving Classification And Recong
• [Theory]
  • Generative Models
  • GAN Theory
• [Machine Learning]
  • Conditional-Adversarial
  • Semi-Supervised Learning
  • Ensemble
• [Others]
  • AutoML
  • Reinforcement learning
  • Discrete Distributions
  • RNN
• [Interdisciplinary]
  • Medicine
  • MUSIC
• [Tutorial]
  • Project
  • Blogs
  • Tutorial

First paper

✔️ [Generative Adversarial Nets]

• [Paper][Code](NIPS 2014)

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)

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

• [Paper](NIPS 2017)[Code]

✔️ [Unsupervised Image-to-Image Translation 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]

✔️ [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]

✔️ [Everybody Dance Now]

• [Paper][Code]

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

• [Paper](CVPR 2019)

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

• [Paper][Code](CVPR 2019 oral)

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

• [Paper][Code](CVPR 2020)

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

• [Paper][Code](CVPR 2020)

✔️ [Structural-analogy from a Single Image Pair]

• [Paper][Code]

✔️ [High-Resolution Daytime Translation Without Domain Labels]

• [Paper][Code]

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

• [Paper][Code]

✔️ [Diverse Image Generation via Self-Conditioned GANs]

• [Paper][Code](CVPR2020)

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

• [Paper][Code](ECCV2020)

Facial Attribute Manipulation

✔️ [Autoencoding beyond pixels using a learned similarity metric]

• [Paper][code][Tensorflow code](ICML 2016）

✔️ [Coupled Generative Adversarial Networks]

• [Paper][Caffe Code][Tensorflow Code](NIPS 2016）

✔️ [Invertible Conditional GANs for image editing]

• [Paper][Code](Arxiv 2016)

✔️ [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)

✔️ [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](TIP 2019)

✔️ [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)

✔️ [Geometry Guided Adversarial Facial Expression Synthesis]

• [Paper](ACM MM2018)

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

• [Paper][code](CVPR 2019)

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

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

• [Paper][code](ICCV 2019)

✔️ [A Survey of Deep Facial Attribute Analysis]

• [Paper](IJCV 2019)

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

• [Paper][code]（Arxiv 2020）

✔️ [SSCGAN: Facial Attribute Editing via StyleSkip Connections]

• [Paper](ECCV 2020)

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

• [Paper](ECCV 2020)

Generative Models

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

• [Paper][Code](Gan with convolutional networks)(ICLR 2015)

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

• [Paper][Code](NIPS 2015)

✔️ [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]

• [Paper][Code]

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

• [Paper][Code][Tensorflow Code]

✔️ [ Self-Attention Generative Adversarial Networks ]

• [Paper][Code](NIPS 2018)

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

• [Paper](ICLR 2019)

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

• [Paper][Code]

✔️ [Analyzing and Improving the Image Quality of StyleGAN]

• [Paper][Code]

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

• [Paper][Code](ICCV2019 best paper)

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

• [Paper][Code](ICLR2020 Spot)

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

• [Paper]

✔️ [Adversarial Latent Autoencoders]

• [Paper][code]

Gaze Correction and Redirection

✔️ [DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation]

• [Paper][code](ECCV 2016)

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

• [Paper][Code](ICCV 2019)

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

• [Paper][code]

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

• [Paper]

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

• [Paper][Code](ACM MM2020)

AutoML

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

• [Paper][Code](ICCV 2019)

Image Animation

✔️ [Animating arbitrary objects via deep motion transfer]

• [Paper][code](CVPR 2019)

✔️ [First Order Motion Model for Image Animation]

• [Paper][code](NIPS 2019)

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）

✔️ [Self-Supervised Generative Adversarial Networks]

• [Paper][code]（CVPR 2019）

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]

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

• [Paper][Code]

Scene Generation

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

• [Paper][Code](AAAI 2019)

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]

Re-identification

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

• [Paper][Code][YouTube] [Bilibili] (CVPR2019 Oral)

✔️ [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)

Landmark Detection

✔️ [Style aggregated network for facial landmark detection]

• [Paper](CVPR 2018)

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]

Shadow Detection and Removal

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

• [Paper][Code](ICCV 2019)

Makeup

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

• [Paper](ACMMM 2018)

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]

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）

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

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

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

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

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]

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

 

原文：https://github.com/zhangqianhui/AdversarialNetsPapers