AdversarialNetsPapers

The classical Papers about adversarial nets

The First paper

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

Unclassified

:white_check_mark: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [Paper][Code]

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

:white_check_mark: [Adversarial Autoencoders] [Paper][Code]

:white_check_mark: [Generating Images with Perceptual Similarity Metrics based on Deep Networks] [Paper]

:white_check_mark: [Generating images with recurrent adversarial networks] [Paper][Code]

:white_check_mark: [Generative Visual Manipulation on the Natural Image Manifold] [Paper][Code]

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

:white_check_mark: [Learning What and Where to Draw] [Paper][Code]

:white_check_mark: [Adversarial Training for Sketch Retrieval] [Paper]

:white_check_mark: [Generative Image Modeling using Style and Structure Adversarial Networks] [Paper][Code]

:white_check_mark: [Generative Adversarial Networks as Variational Training of Energy Based Models] [Paper](ICLR 2017)

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

:white_check_mark: [Learning from Simulated and Unsupervised Images through Adversarial Training] [Paper][code]（Apple paper）

:white_check_mark: [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks] [Paper][Code]

:white_check_mark: [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [Paper][Code]

:white_check_mark: [Adversarial Feature Learning] [Paper]

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

Ensemble

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

Clustering

:white_check_mark: [Unsupervised Learning Using Generative Adversarial Training And Clustering] [Paper][Code](ICLR) :white_check_mark: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper](ICLR)

Image Inpainting

:white_check_mark: [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code]

:white_check_mark: [Context Encoders: Feature Learning by Inpainting] [Paper][Code]

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

Joint Probability

:white_check_mark: [Adversarially Learned Inference][Paper][Code]

Super-Resolution

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

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

:white_check_mark: [EnhanceGAN] [Docs][[Code]]

Disocclusion

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

Semantic Segmentation

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

Object Detection

:white_check_mark: [Perceptual generative adversarial networks for small object detection] [[Paper]]（Submitted）

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

RNN

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

Conditional adversarial

:white_check_mark: [Conditional Generative Adversarial Nets] [Paper][Code]

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

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

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

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

:white_check_mark: [Invertible Conditional GANs for image editing] [Paper][Code]

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

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

:white_check_mark: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper]

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

Video Prediction

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

:white_check_mark: [Unsupervised Learning for Physical Interaction through Video Prediction] [Paper](Ian Goodfellow's paper)

:white_check_mark: [Generating Videos with Scene Dynamics] [Paper][Web][Code]

Texture Synthesis & style transfer

:white_check_mark: [Precomputed real-time texture synthesis with markovian generative adversarial networks] [Paper][Code](ECCV 2016)

GAN Theory

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

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

:white_check_mark: [Mode RegularizedGenerative Adversarial Networks] [Paper](Yoshua Bengio , ICLR 2017)

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

:white_check_mark: [Sampling Generative Networks] [Paper][Code]

:white_check_mark: [Mode Regularized Generative Adversarial Networkss] [Paper]( Yoshua Bengio's paper)

:white_check_mark: [How to train Gans] [Docu]

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

:white_check_mark: [Unrolled Generative Adversarial Networks] [Paper][Code]

:white_check_mark: [Least Squares Generative Adversarial Networks] [Paper][Code]

:white_check_mark: [Wasserstein GAN] [Paper][Code]

:white_check_mark: [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities] [Paper][Code](The same as WGan)

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

3D

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

Face Generative and Editing

:white_check_mark: [Autoencoding beyond pixels using a learned similarity metric] [Paper][code]

:white_check_mark: [Coupled Generative Adversarial Networks] [Paper][Caffe Code][Tensorflow Code]（NIPS）

:white_check_mark: [Invertible Conditional GANs for image editing] [Paper][Code]

:white_check_mark: [Learning Residual Images for Face Attribute Manipulation] [Paper]

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

For discrete distributions

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

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

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

Project

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

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

:white_check_mark: [HyperGAN] [Code](Open source GAN focused on scale and usability)

Blogs

Other

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

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