(转) AdversarialNetsPapers

 

本文转自：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)

✅ [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

✅ [1] http://www.inference.vc/

✅ [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)

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