The classical Papers about adversarial nets
✅ [Generative Adversarial Nets] [Paper] [Code]
✅ [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [Paper][Code]
✅ [Conditional Generative Adversial Nets] [Paper]
✅ [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code]
✅ [Autoencoding beyond pixels using a learned similarity metric] [Paper]
✅ [Adversarial Autoencoders] [Paper][Code]
✅ [Generating images with recurrent adversarial networks] [Paper][Code]
✅ [Generative Visual Manipulation on the Natural Image Manifold] [Paper][Code]
✅ [Deep multi-scale video prediction beyond mean square error] [Paper][Code]
✅ [Energy-based generative adversarial network] [Paper][Code]
✅ [Neural Photo Editing with Introspective Adversarial Networks] [Paper]
✅ [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [Paper][Code]
✅ [Improved Techniques for Training GANs] [Paper][Code]
✅ [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code]
✅ [Generative Adversarial Text to Image Synthesis] [Paper][Code]
✅ [Learning What and Where to Draw] [Paper][Code]
✅ [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [Paper][Code]
✅ [Adversarial Training for Sketch Retrieval] [Paper]
✅ [Generative Image Modeling using Style and Structure Adversarial Networks] [Paper][Code]
#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]
#Project
✅ [cleverhans] [Code](A library for benchmarking vulnerability to adversarial examples)