AdversarialNetsPapers

The good papers about Generative Adversarial Networks

The First paper

✅ [Generative Adversarial Nets] [Paper] [Code](the first paper about it)

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]

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]

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)

Ensemble

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

Clustering

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

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](SIGGRAPH 2017)

Joint Probability

✅ [Adversarially Learned Inference][Paper][Code]

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]]

Disocclusion

✅ [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)

RNN

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

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]

✅ [Unsupervised Image-to-Image Translation Networks] [Paper]

✅ [Triangle Generative Adversarial Networks] [Paper]

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）

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]

Face Generative and Editing

✅ [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](BMVC 2017)[code]

✅ [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]

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

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]