AdversarialNetsPapers
A collection of resources and papers on Generation Adversarial Networks.
Table of Contents
- First Paper
- [Application]
- [Theory]
- [Machine Learning]
- [Others]
- [Interdisciplinary]
- [Tutorial]
First paper
✔️ [Generative Adversarial Nets]
Image Translation
✔️ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION]
✔️ [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]
AutoML
✔️ [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]
✔️ [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]
Ensemble
✔️ [AdaGAN: Boosting Generative Models]
Image blending
✔️ [GP-GAN: Towards Realistic High-Resolution Image Blending]
Re-identification
✔️ [Joint Discriminative and Generative Learning for Person Re-identification]
✔️ [Pose-Normalized Image Generation for Person Re-identification]
Super-Resolution
✔️ [Image super-resolution through deep learning]
✔️ [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network]
✔️ [EnhanceGAN]
✔️ [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks]
De-Occlusion
✔️ [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]
Makeup
✔️ [BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network]
Reinforcement learning
✔️ [Connecting Generative Adversarial Networks and Actor-Critic Methods]
RNN
✔️ [C-RNN-GAN: Continuous recurrent neural networks with adversarial training]
✔️ [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient]
Medicine
✔️ [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery]
3D
✔️ [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling]
✔️ [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis]
MUSIC
✔️ [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）
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]
✔️ [3] [A Mathematical Introduction to Generative Adversarial Nets (GAN)]
