AdversarialNetsPapers

by zhangqianhui

Awesome paper list with code about generative adversarial nets

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AdversarialNetsPapers

Awesome papers about Generative Adversarial Networks. Majority of papers are related to Image Translation.

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Table of Contents

First paper

:heavycheckmark: [Generative Adversarial Nets] - [Paper][Code](NIPS 2014)

Image Translation

:heavycheckmark: [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] - [Paper][Code]

:heavycheckmark: [Image-to-image translation using conditional adversarial nets] - [Paper][Code][Code]

:heavycheckmark: [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] - [Paper][Code]

:heavycheckmark: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] - [Paper][Code]

:heavycheckmark: [CoGAN: Coupled Generative Adversarial Networks] - [Paper][Code](NIPS 2016)

:heavycheckmark: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] - [Paper](NIPS 2017)

:heavycheckmark: [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation] - [Paper](NIPS 2017)[Code]

:heavycheckmark: [Unsupervised Image-to-Image Translation Networks] - [Paper]

:heavycheckmark: [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs] - [Paper][code]

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

:heavycheckmark: [UNIT: UNsupervised Image-to-image Translation Networks] - [Paper][Code](NIPS 2017)

:heavycheckmark: [Toward Multimodal Image-to-Image Translation] - [Paper][Code](NIPS 2017)

:heavycheckmark: [Multimodal Unsupervised Image-to-Image Translation] - [Paper][Code]

:heavycheckmark: [Video-to-Video Synthesis] - [Paper][Code]

:heavycheckmark: [Everybody Dance Now] - [Paper][Code]

:heavycheckmark: [Art2Real: Unfolding the Reality of Artworks via Semantically-Aware Image-to-Image Translation] - [Paper](CVPR 2019)

:heavycheckmark: [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation] - [Paper][Code](CVPR 2019 oral)

:heavycheckmark: [Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation] - [Paper][Code](CVPR 2020)

:heavycheckmark: [StarGAN v2: Diverse Image Synthesis for Multiple Domains] - [Paper][Code](CVPR 2020)

:heavycheckmark: [Structural-analogy from a Single Image Pair] - [Paper][Code]

:heavycheckmark: [High-Resolution Daytime Translation Without Domain Labels] - [Paper][Code]

:heavycheckmark: [Rethinking the Truly Unsupervised Image-to-Image Translation] - [Paper][Code]

:heavycheckmark: [Diverse Image Generation via Self-Conditioned GANs] - [Paper][Code](CVPR2020)

:heavycheckmark: [Contrastive Learning for Unpaired Image-to-Image Translation] - [Paper][Code](ECCV2020)

Facial Attribute Manipulation

:heavycheckmark: [Autoencoding beyond pixels using a learned similarity metric] - [Paper][code][Tensorflow code](ICML 2016)

:heavycheckmark: [Coupled Generative Adversarial Networks] - [Paper][Caffe Code][Tensorflow Code](NIPS 2016)

:heavycheckmark: [Invertible Conditional GANs for image editing] - [Paper][Code](Arxiv 2016)

:heavycheckmark: [Learning Residual Images for Face Attribute Manipulation] - [Paper][code](CVPR 2017)

:heavycheckmark: [Neural Photo Editing with Introspective Adversarial Networks] - [Paper][Code](ICLR 2017)

:heavycheckmark: [Neural Face Editing with Intrinsic Image Disentangling] - [Paper](CVPR 2017)

:heavycheckmark: [GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] - [Paper][code](BMVC 2017)

:heavycheckmark: [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] - [Paper](ICCV 2017)

:heavycheckmark: [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation] - [Paper][code](CVPR 2018)

:heavycheckmark: [Arbitrary Facial Attribute Editing: Only Change What You Want] - [Paper][code](TIP 2019)

:heavycheckmark: [ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes] - [Paper][code](ECCV 2018)

:heavycheckmark: [Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation] - [Paper][code](ACM MM2018 oral)

:heavycheckmark: [GANimation: Anatomically-aware Facial Animation from a Single Image] - [Paper][code](ECCV 2018 oral)

:heavycheckmark: [Geometry Guided Adversarial Facial Expression Synthesis] - [Paper](ACM MM2018)

:heavycheckmark: [STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing] - [Paper][code](CVPR 2019)

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

:heavycheckmark: [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color] - [Paper][code](ICCV 2019)

:heavycheckmark: [A Survey of Deep Facial Attribute Analysis] - [Paper](IJCV 2019)

:heavycheckmark: [PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing] - [Paper][code](Arxiv 2020)

:heavycheckmark: [SSCGAN: Facial Attribute Editing via StyleSkip Connections] - [Paper](ECCV 2020)

:heavycheckmark: [CAFE-GAN: Arbitrary Face Attribute Editingwith Complementary Attention Feature] - [Paper](ECCV 2020)

Generative Models

:heavycheckmark: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] - [Paper][Code](Gan with convolutional networks)(ICLR 2015)

:heavycheckmark: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] - [Paper][Code](NIPS 2015)

:heavycheckmark: [Generative Adversarial Text to Image Synthesis] - [Paper][Code][code]

:heavycheckmark: [Improved Techniques for Training GANs] - [Paper][Code](Goodfellow's paper)

:heavycheckmark: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] - [Paper][Code]

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

:heavycheckmark: [Improved Training of Wasserstein GANs] - [Paper][Code]

:heavycheckmark: [Boundary Equibilibrium Generative Adversarial Networks] - [Paper][Code]

:heavycheckmark: [Progressive Growing of GANs for Improved Quality, Stability, and Variation] - [Paper][Code][Tensorflow Code]

:heavycheckmark: [ Self-Attention Generative Adversarial Networks ] - [Paper][Code](NIPS 2018)

:heavycheckmark: [Large Scale GAN Training for High Fidelity Natural Image Synthesis] - [Paper](ICLR 2019)

:heavycheckmark: [A Style-Based Generator Architecture for Generative Adversarial Networks] - [Paper][Code]

:heavycheckmark: [Analyzing and Improving the Image Quality of StyleGAN] - [Paper][Code]

:heavycheckmark: [SinGAN: Learning a Generative Model from a Single Natural Image] - [Paper][Code](ICCV2019 best paper)

:heavycheckmark: [Real or Not Real, that is the Question] - [Paper][Code](ICLR2020 Spot)

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

:heavycheckmark: [Adversarial Latent Autoencoders] - [Paper][code]

Gaze Correction and Redirection

:heavycheckmark: [DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation] - [Paper][code](ECCV 2016)

:heavycheckmark: [Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks] - [Paper][Code](ICCV 2019)

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

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

:heavycheckmark: [Dual In-painting Model for Unsupervised Gaze Correction and Animation in the Wild] - [Paper][Code](ACM MM2020)

AutoML

:heavycheckmark: [AutoGAN: Neural Architecture Search for Generative Adversarial Networks] - [Paper][Code](ICCV 2019)

Image Animation

:heavycheckmark: [Animating arbitrary objects via deep motion transfer] - [Paper][code](CVPR 2019)

:heavycheckmark: [First Order Motion Model for Image Animation] - [Paper][code](NIPS 2019)

GAN Theory

:heavycheckmark: [Energy-based generative adversarial network] - [Paper][Code](Lecun paper)

:heavycheckmark: [Improved Techniques for Training GANs] - [Paper][Code](Goodfellow's paper)

:heavycheckmark: [Mode Regularized Generative Adversarial Networks] - [Paper](Yoshua Bengio , ICLR 2017)

:heavycheckmark: [Improving Generative Adversarial Networks with Denoising Feature Matching] - [Paper][Code](Yoshua Bengio , ICLR 2017)

:heavycheckmark: [Sampling Generative Networks] - [Paper][Code]

:heavycheckmark: [How to train Gans] - [Docu]

:heavycheckmark: [Towards Principled Methods for Training Generative Adversarial Networks] - [Paper](ICLR 2017)

:heavycheckmark: [Unrolled Generative Adversarial Networks] - [Paper][Code](ICLR 2017)

:heavycheckmark: [Least Squares Generative Adversarial Networks] - [Paper][Code](ICCV 2017)

:heavycheckmark: [Wasserstein GAN] - [Paper][Code]

:heavycheckmark: [Improved Training of Wasserstein GANs] - [Paper][Code](The improve of wgan)

:heavycheckmark: [Towards Principled Methods for Training Generative Adversarial Networks] - [Paper]

:heavycheckmark: [Generalization and Equilibrium in Generative Adversarial Nets] - [Paper](ICML 2017)

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

:heavycheckmark: [Spectral Normalization for Generative Adversarial Networks] - [Paper][code](ICLR 2018)

:heavycheckmark: [Which Training Methods for GANs do actually Converge] - [Paper][code](ICML 2018)

:heavycheckmark: [Self-Supervised Generative Adversarial Networks] - [Paper][code](CVPR 2019)

Image Inpainting

:heavycheckmark: [Semantic Image Inpainting with Perceptual and Contextual Losses] - [Paper][Code](CVPR 2017)

:heavycheckmark: [Context Encoders: Feature Learning by Inpainting] - [Paper][Code]

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

:heavycheckmark: [Generative face completion] - [Paper][code](CVPR2017)

:heavycheckmark: [Globally and Locally Consistent Image Completion] - [MainPAGE][code](SIGGRAPH 2017)

:heavycheckmark: [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis] - [Paper][code](CVPR 2017)

:heavycheckmark: [Eye In-Painting with Exemplar Generative Adversarial Networks] - [Paper][Introduction][Tensorflow code](CVPR2018)

:heavycheckmark: [Generative Image Inpainting with Contextual Attention] - [Paper][Project][Demo][YouTube][Code](CVPR2018)

:heavycheckmark: [Free-Form Image Inpainting with Gated Convolution] - [Paper][Project][YouTube]

:heavycheckmark: [EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning] - [Paper][Code]

Scene Generation

:heavycheckmark: [a layer-based sequential framework for scene generation with gans] - [Paper][Code](AAAI 2019)

Semi-Supervised Learning

:heavycheckmark: [Adversarial Training Methods for Semi-Supervised Text Classification] - [Paper][Note]( Ian Goodfellow Paper)

:heavycheckmark: [Improved Techniques for Training GANs] - [Paper][Code](Goodfellow's paper)

:heavycheckmark: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] - [Paper](ICLR)

:heavycheckmark: [Semi-Supervised QA with Generative Domain-Adaptive Nets] - [Paper](ACL 2017)

:heavycheckmark: [Good Semi-supervised Learning that Requires a Bad GAN] - [Paper][Code](NIPS 2017)

Ensemble

:heavycheckmark: [AdaGAN: Boosting Generative Models] - [Paper][[Code]](Google Brain)

Image blending

:heavycheckmark: [GP-GAN: Towards Realistic High-Resolution Image Blending] - [Paper][Code]

Re-identification

:heavycheckmark: [Joint Discriminative and Generative Learning for Person Re-identification] - [Paper][Code][YouTube] [Bilibili] (CVPR2019 Oral)

:heavycheckmark: [Pose-Normalized Image Generation for Person Re-identification] - [Paper][Code](ECCV 2018)

Super-Resolution

:heavycheckmark: [Image super-resolution through deep learning] - [Code](Just for face dataset)

:heavycheckmark: [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] - [Paper][Code](Using Deep residual network)

:heavycheckmark: [EnhanceGAN] - [Docs][[Code]]

:heavycheckmark: [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks] - [Paper][Code](ECCV 2018 workshop)

De-Occlusion

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

Semantic Segmentation

:heavycheckmark: [Adversarial Deep Structural Networks for Mammographic Mass Segmentation] - [Paper][Code]

:heavycheckmark: [Semantic Segmentation using Adversarial Networks] - [Paper](soumith's paper)

Object Detection

:heavycheckmark: [Perceptual generative adversarial networks for small object detection] - [Paper](CVPR 2017)

:heavycheckmark: [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] - [Paper][code](CVPR2017)

Landmark Detection

:heavycheckmark: [Style aggregated network for facial landmark detection] - [Paper](CVPR 2018)

Conditional Adversarial

:heavycheckmark: [Conditional Generative Adversarial Nets] - [Paper][Code]

:heavycheckmark: [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] - [Paper][Code][Code]

:heavycheckmark: [Conditional Image Synthesis With Auxiliary Classifier GANs] - [Paper][Code](GoogleBrain ICLR 2017)

:heavycheckmark: [Pixel-Level Domain Transfer] - [Paper][Code]

:heavycheckmark: [Invertible Conditional GANs for image editing] - [Paper][Code]

:heavycheckmark: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] - [Paper][Code]

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

Video Prediction and Generation

:heavycheckmark: [Deep multi-scale video prediction beyond mean square error] - [Paper][Code](Yann LeCun's paper)

:heavycheckmark: [Generating Videos with Scene Dynamics] - [Paper][Web][Code]

:heavycheckmark: [MoCoGAN: Decomposing Motion and Content for Video Generation] - [Paper]

Shadow Detection and Removal

:heavycheckmark: [ARGAN: Attentive Recurrent Generative Adversarial Network for Shadow Detection and Removal] - [Paper][Code](ICCV 2019)

Makeup

:heavycheckmark: [BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network] - [Paper](ACMMM 2018)

Reinforcement learning

:heavycheckmark: [Connecting Generative Adversarial Networks and Actor-Critic Methods] - [Paper](NIPS 2016 workshop)

RNN

:heavycheckmark: [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] - [Paper][Code]

:heavycheckmark: [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient] - [Paper][Code](AAAI 2017)

Medicine

:heavycheckmark: [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery] - [Paper]

3D

:heavycheckmark: [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] - [Paper][Web][code](2016 NIPS)

:heavycheckmark: [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] - [Web](CVPR 2017)

MUSIC

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

Discrete distributions

:heavycheckmark: [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] - [Paper]

:heavycheckmark: [Boundary-Seeking Generative Adversarial Networks] - [Paper]

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

Improving Classification And Recong

:heavycheckmark: [Generative OpenMax for Multi-Class Open Set Classification] - [Paper](BMVC 2017)

:heavycheckmark: [Controllable Invariance through Adversarial Feature Learning] - [Paper][code](NIPS 2017)

:heavycheckmark: [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] - [Paper][Code] (ICCV2017)

:heavycheckmark: [Learning from Simulated and Unsupervised Images through Adversarial Training] - [Paper][code](Apple paper, CVPR 2017 Best Paper)

:heavycheckmark: [GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification] - [Paper] (Neurocomputing Journal (2018), Elsevier)

Project

:heavycheckmark: [cleverhans] - [Code](A library for benchmarking vulnerability to adversarial examples)

:heavycheckmark: [reset-cppn-gan-tensorflow] - [Code](Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images)

:heavycheckmark: [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

:heavycheckmark: [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]

:heavycheckmark: [2] [PDF](NIPS Lecun Slides)

:heavycheckmark: [3] [ICCV 2017 Tutorial About GANS]

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

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