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The classical Papers about adversarial nets
[Generative Adversarial Nets] (the first paper about it)
[Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks]
[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] (Gan with convolutional networks)
[Adversarial Autoencoders]
[Generating images with recurrent adversarial networks]
[Generative Visual Manipulation on the Natural Image Manifold]
[Neural Photo Editing with Introspective Adversarial Networks]
[Generative Adversarial Text to Image Synthesis]
[Learning What and Where to Draw]
[Adversarial Training for Sketch Retrieval]
[Generative Image Modeling using Style and Structure Adversarial Networks]
[Generative Adversarial Networks as Variational Training of Energy Based Models] (ICLR 2017)
[Towards Principled Methods for Training Generative Adversarial Networks] (ICLR 2017)
[Adversarial Training Methods for Semi-Supervised Text Classification] ( Ian Goodfellow Paper)
[Learning from Simulated and Unsupervised Images through Adversarial Training] （Apple paper）
[Synthesizing the preferred inputs for neurons in neural networks via deep generator networks]
[SalGAN: Visual Saliency Prediction with Generative Adversarial Networks]
[Semantic Image Inpainting with Perceptual and Contextual Losses]
[Context Encoders: Feature Learning by Inpainting]
[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] （Using Deep residual network）
[Robust LSTM-Autoencoders for Face De-Occlusion in the Wild]
[Semantic Segmentation using Adversarial Networks] （soumith's paper）
[Perceptual generative adversarial networks for small object detection] [[Paper]]（Submitted）
[C-RNN-GAN: Continuous recurrent neural networks with adversarial training]
[Conditional Generative Adversarial Nets]
[InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets]
[Image-to-image translation using conditional adversarial nets]
[Conditional Image Synthesis With Auxiliary Classifier GANs] (GoogleBrain ICLR 2017)
[Pixel-Level Domain Transfer]
[Invertible Conditional GANs for image editing]
[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space]
[StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks]
[Deep multi-scale video prediction beyond mean square error] (Yann LeCun's paper)
[Unsupervised Learning for Physical Interaction through Video Prediction] (Ian Goodfellow's paper)
[Generating Videos with Scene Dynamics]
[Precomputed real-time texture synthesis with markovian generative adversarial networks] (ECCV 2016)
[Energy-based generative adversarial network] (Lecun paper)
[Improved Techniques for Training GANs] (Goodfellow's paper)
[Mode RegularizedGenerative Adversarial Networks] (Yoshua Bengio , ICLR 2017)
[Improving Generative Adversarial Networks with Denoising Feature Matching] (Yoshua Bengio , ICLR 2017)
[Sampling Generative Networks]
[Mode Regularized Generative Adversarial Networkss] ( Yoshua Bengio's paper)
[How to train Gans]
[Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] (2016 NIPS)
[Autoencoding beyond pixels using a learned similarity metric]
[Coupled Generative Adversarial Networks] （NIPS）
[Intriguing properties of neural networks]
[Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images]
[Explaining and Harnessing Adversarial Examples]
[Adversarial examples in the physical world]
[Universal adversarial perturbations ]
[Robustness of classifiers: from adversarial to random noise ]
[DeepFool: a simple and accurate method to fool deep neural networks]
[2] (NIPS Goodfellow Slides)
[cleverhans] (A library for benchmarking vulnerability to adversarial examples)
[reset-cppn-gan-tensorflow] (Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images)
[HyperGAN] (Open source GAN focused on scale and usability)
[1]
[2]
[1] (NIPS Goodfellow Slides)
[2] (NIPS Lecun Slides)
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