The classical Papers about adversarial nets
##The First paper :white_check_mark: [Generative Adversarial Nets] [Paper] [Code](the first paper about it)
##Unclassified
✅ [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [Paper][Code]
✅ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)
✅ [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]
✅ [Neural Photo Editing with Introspective Adversarial Networks] [Paper]
✅ [Generative Adversarial Text to Image Synthesis] [Paper][Code][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)
✅ [Adversarial Training Methods for Semi-Supervised Text Classification] [Paper][Note]( Ian Goodfellow Paper)
✅ [Learning from Simulated and Unsupervised Images through Adversarial Training] [Paper][code]（Apple paper）
✅ [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]
##Ensemble
✅ [AdaGAN: Boosting Generative Models] [Paper][[Code]]（Google Brain）
##Image Inpainting
✅ [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code]
✅ [Context Encoders: Feature Learning by Inpainting] [Paper][Code]
✅ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper]
##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）
##Disocclusion
✅ [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [Paper]
##Semantic Segmentation
✅ [Semantic Segmentation using Adversarial Networks] [Paper]（soumith's paper）
##Object Detection
✅ [Perceptual generative adversarial networks for small object detection] [[Paper]]（Submitted）
##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]
✅ [Image-to-image translation using conditional 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
✅ [Deep multi-scale video prediction beyond mean square error] [Paper][Code](Yann LeCun's paper)
✅ [Unsupervised Learning for Physical Interaction through Video Prediction] [Paper](Ian Goodfellow's paper)
✅ [Generating Videos with Scene Dynamics] [Paper][Web][Code]
##Texture Synthesis && style transfer
✅ [Precomputed real-time texture synthesis with markovian generative adversarial networks] [Paper][Code](ECCV 2016)
##GAN Theory
✅ [Energy-based generative adversarial network] [Paper][Code](Lecun paper)
✅ [Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)
✅ [Mode RegularizedGenerative 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]
✅ [Mode Regularized Generative Adversarial Networkss] [Paper]( Yoshua Bengio's paper)
✅ [How to train Gans] [Docu]
✅ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper](ICLR 2017)
✅ [Unrolled Generative Adversarial Networks] [Paper][Code]
✅ [Wasserstein GAN] [Paper][Code]
✅ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper]
##3D
✅ [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [Paper][Web][code](2016 NIPS)
##Face Generative
✅ [Autoencoding beyond pixels using a learned similarity metric] [Paper][code]
✅ [Coupled Generative Adversarial Networks] [Paper][Caffe Code][Tensorflow Code]（NIPS）
#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 |
#Other
✅ [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]
✅ [2] [PDF](NIPS Lecun Slides)
#Adversarial Examples
| Title | Paper | Code | |---- | ---|----|----| | Intriguing properties of neural networks | Paper |[Code]| | Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images | Paper |[Code]| | Explaining and Harnessing Adversarial Examples | Paper |[Code]| | Adversarial examples in the physical world | Paper |[Code]| | Universal adversarial perturbations | Paper |[Code]| | Robustness of classifiers: from adversarial to random noise | Paper |[Code]| | DeepFool: a simple and accurate method to fool deep neural networks | Paper |[Code]| | Goodfellow Slides | Paper |[Code]| | The Limitations of Deep Learning in Adversarial Settings | Paper |Code| | Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples | Paper |[Code]|