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https://github.com/zhangqianhui/AdversarialNetsPapers
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)(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]
Ensemble
[AdaGAN: Boosting Generative Models] [Paper][[Code]]（Google Brain）
Clustering
[Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper](ICLR)
Image blending
[GP-GAN: Towards Realistic High-Resolution Image Blending] [Paper][Code]
Image Inpainting
[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)
Joint Probability
[Adversarially Learned Inference][Paper][Code]
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）
[EnhanceGAN] [Docs][[Code]]
Disocclusion
[Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [Paper]
Semantic Segmentation
[Adversarial Deep Structural Networks for Mammographic Mass Segmentation] [Paper][Code]
[Semantic Segmentation using Adversarial Networks] [Paper]（soumith's paper）
Object Detection
[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)
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][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)
[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)
Image translation
[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]
[Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper]
[Unsupervised Image-to-Image Translation Networks] [Paper]
GAN Theory
[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]
[Wasserstein GAN] [Paper][Code]
[Improved Training of Wasserstein GANs] [Paper][Code](The improve of wgan)
[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)
[Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] [Web](CVPR 2017)
MUSIC
[MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] [Paper][HOMEPAGE]
Face Generative and Editing
[Autoencoding beyond pixels using a learned similarity metric] [Paper][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)
For discrete distributions
[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]
Adversarial Examples
[SafetyNet: Detecting and Rejecting Adversarial Examples Robustly] [Paper]
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)
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