AdversarialNetsPapers
The classical Papers about adversarial nets
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]
[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)
[Adversarial Autoencoders] [Paper][Code]
[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)
[Towards Principled Methods for Training Generative Adversarial Networks] [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]
Image Inpainting
[Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code]
[Context Encoders: Feature Learning by Inpainting] [Paper][Code]
Super-Resolution
[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]
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）
Adversarial Examples
[Intriguing properties of neural networks] [Paper]
[Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images] [Paper]
[Explaining and Harnessing Adversarial Examples] [Paper]
[Adversarial examples in the physical world] [Paper]
[Universal adversarial perturbations ] [Paper]
[Robustness of classifiers: from adversarial to random noise ] [Paper]
[DeepFool: a simple and accurate method to fool deep neural networks] [Paper]
[2] [PDF] (NIPS Goodfellow Slides)
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
[2] http://distill.pub/2016/deconv-checkerboard/
Other
[1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]
[2] [PDF](NIPS Lecun Slides)
