The classical Papers about adversarial nets
✅ [Generative Adversarial Nets] [Paper]
[Code] (the first paper about it)
✅ [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] [Paper] [Code] (ICCV2017)
✅ [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)(ICLR)
✅ [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]
✅ [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, CVPR 2017 Best 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]
✅ [AdaGAN: Boosting Generative Models] [Paper] [[Code]]（Google Brain）
✅ [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper] (ICLR)
✅ [GP-GAN: Towards Realistic High-Resolution Image Blending] [Paper] [Code]
✅ [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper] [Code] (CVPR 2017)
✅ [Context Encoders: Feature Learning by Inpainting] [Paper] [Code]
✅ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper]
✅ [Generative face completion] [Paper] [code] (CVPR2017)
✅ [Globally and Locally Consistent Image Completion] [MainPAGE] (SIGGRAPH 2017)
✅ [Adversarially Learned Inference][Paper] [Code]
✅ [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）
✅ [EnhanceGAN] [Docs] [[Code]]
✅ [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [Paper]
✅ [Adversarial Deep Structural Networks for Mammographic Mass Segmentation] [Paper] [Code]
✅ [Semantic Segmentation using Adversarial Networks] [Paper] （soumith's paper）
✅ [Perceptual generative adversarial networks for small object detection] [Paper] (CVPR 2017)
✅ [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [Paper] [code] (CVPR2017)
✅ [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] [Paper] [Code]
✅ [Conditional Generative Adversarial Nets] [Paper] [Code]
✅ [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative 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]
✅ [Deep multi-scale video prediction beyond mean square error] [Paper] [Code] (Yann LeCun's paper)
✅ [Generating Videos with Scene Dynamics] [Paper] [Web] [Code]
✅ [MoCoGAN: Decomposing Motion and Content for Video Generation] [Paper]
✅ [Precomputed real-time texture synthesis with markovian generative adversarial networks] [Paper] [Code] (ECCV 2016)
✅ [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] [Paper] [Code]
✅ [Image-to-image translation using conditional adversarial nets] [Paper] [Code] [Code]
✅ [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [Paper] [Code]
✅ [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [Paper] [Code]
✅ [CoGAN: Coupled Generative Adversarial Networks] [Paper] [Code] (NIPS 2016)
✅ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper]
✅ [Unsupervised Image-to-Image Translation Networks] [Paper]
✅ [Triangle Generative Adversarial Networks] [Paper]
✅ [Energy-based generative adversarial network] [Paper] [Code] (Lecun paper)
✅ [Improved Techniques for Training GANs] [Paper] [Code] (Goodfellow's paper)
✅ [Mode Regularized Generative 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]
✅ [How to train Gans] [Docu]
✅ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper] (ICLR 2017)
✅ [Unrolled Generative Adversarial Networks] [Paper] [Code] (ICLR 2017)
✅ [Least Squares Generative Adversarial Networks] [Paper] [Code] (ICCV 2017)
✅ [Wasserstein GAN] [Paper] [Code]
✅ [Improved Training of Wasserstein GANs] [Paper] [Code] (The improve of wgan)
✅ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper]
✅ [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [Paper] [Web] [code] (2016 NIPS)
✅ [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] [Web] (CVPR 2017)
✅ [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] [Paper] [HOMEPAGE]
✅ [Autoencoding beyond pixels using a learned similarity metric] [Paper] [code] [Tensorflow code]
✅ [Coupled Generative Adversarial Networks] [Paper] [Caffe Code] [Tensorflow Code] （NIPS）
✅ [Invertible Conditional GANs for image editing] [Paper] [Code]
✅ [Learning Residual Images for Face Attribute Manipulation] [Paper] [code] (CVPR 2017)
✅ [Neural Photo Editing with Introspective Adversarial Networks] [Paper] [Code] (ICLR 2017)
✅ [Neural Face Editing with Intrinsic Image Disentangling] [Paper] (CVPR 2017)
✅ [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] [Paper] (ICCV 2017)
✅ [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [Paper]
✅ [Boundary-Seeking Generative Adversarial Networks] [Paper]
✅ [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] [Paper]
✅ [SafetyNet: Detecting and Rejecting Adversarial Examples Robustly] [Paper]
✅ [Generative OpenMax for Multi-Class Open Set Classification] [Paper] (BMVC 2017)
✅ [Controllable Invariance through Adversarial Feature Learning] [Paper] [code] (NIPS 2017)
✅ [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)
✅ [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans] [details]
✅ [2] [PDF] (NIPS Lecun Slides)