Papers, codes, slides and blogs about Generative Adversrial Nets.
✅ [Generative Adversarial Nets] [Paper] [Code](the first paper about it)
◻️ [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper]
◻️ [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）
◻️ [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）
◻️ [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code]
◻️ [Context Encoders: Feature Learning by Inpainting] [Paper][Code]
◻️ [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper]
◻️ [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]
◻️ [Semantic Segmentation using Adversarial Networks] [Paper]（soumith's paper）
◻️ [Perceptual generative adversarial networks for small object detection] [[Paper]]（Submitted）
◻️ [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]
◻️ [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]
◻️ [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper]
◻️ [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)
◻️ [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]
◻️ [Towards Principled Methods for Training Generative Adversarial Networks] [Paper](ICLR 2017)
◻️ [Unrolled Generative Adversarial Networks] [Paper][Code]
✅ [Wasserstein GAN] [Paper][Code]
◻️ [Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities] [Paper][Code](The same as 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)
##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]
◻️ [Neural Photo Editing with Introspective Adversarial Networks] [Paper][Code](ICLR 2017)
◻️ [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [Paper]
◻️ [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)
| Author | Address | |---- | ---|----| | inFERENCe | Adversarial network | | inFERENCe | InfoGan | | distill | Deconvolution and Image Generation | | yingzhenli | Gan theory | | OpenAI | Generative model |
◻️ [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]
◻️ [2] [PDF](NIPS Lecun Slides)
| Title | Paper | Code | |---- | ---|----|----| | Intriguing properties of neural networks | Paper |[Code]| | Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images | Paper |[Code]| | Explaining and Harnessing Adversarial Examples | Paper |[Code]| | Adversarial examples in the physical world | Paper |[Code]| | Universal adversarial perturbations | Paper |[Code]| | Robustness of classifiers: from adversarial to random noise | Paper |[Code]| | DeepFool: a simple and accurate method to fool deep neural networks | Paper |[Code]| | Goodfellow Slides | Paper |[Code]| | The Limitations of Deep Learning in Adversarial Settings | Paper |Code| | Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples | Paper |[Code]|
2014 GAN 《Generative Adversarial Networks》-Ian Goodfellow, arXiv:1406.2661v1
2014 CGAN 《Conditional Generative Adversarial Nets》- Mehdi Mirza, arXiv:1411.1784v1
2015 LAPGAN 《Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks》- Emily Denton & Soumith Chintala, arxiv: 1506.05751
2015 SRGAN《super-resolution generative adversarial network》- Joan Bruna, Pablo Sprechmann, Yann LeCun , arXiv:1511.05666
2015《Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks》- Jost Tobias Springenberg ,arXiv:1511.06390
2015 DCGAN《Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks》 - Alec Radford & Luke Metz, arxiv:1511.06434
2015 VAEGAN 《Autoencoding beyond pixels using a learned similarity metric》 - Anders Boesen Lindbo Larsen, arxiv: 1512.09300
2016《Generating Images with Recurrent Adversarial Networks》- Daniel Jiwoong Im, Chris Dongjoo Kim ,arXiv:1602.05110
2016《Generative Adversarial Text to Image Synthesis》（“GANs 文字到图像的合成”）- Scott Reed ，arXiv:1605.05396
2016 InfoGAN《InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsI》- Xi Chen, arxiv: 1606.03657
2016 COGAN《Coupled Generative Adversarial Networks》Ming-Yu Liu, Oncel Tuzel - arXiv:1606.07536
2016 EBGAN《Energy-based Generative Adversarial Network》- Junbo Zhao ， arXiv:1609.03126v2
2016 《Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network》 - Christian Ledig, Lucas Theis , arXiv:1609.04802
2016 SeqGAN《SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient》- Lantao Yu, arxiv: 1609.05473
2016《 Contextual RNN-GANs for Abstract Reasoning Diagram Generation》 - Arnab Ghosh, Viveka Kulharia ,arXiv:1609.09444
2016《Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling》- Jiajun Wu, Chengkai Zhang ,arXiv:1610.07584
2016 TGAN《Temporal Generative Adversarial Nets》- Masaki Saito, Eiichi Matsumoto，arXiv:1611.06624
2016 SAD-GAN《SAD-GAN: Synthetic Autonomous Driving using Generative Adversarial Networks》- Arna Ghosh, Biswarup Bhattacharya, Somnath Basu Roy Chowdhury ，arXiv:1611.08788
2016 PPGAN 《Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space》 - Anh Nguyen , arXiv:1612.00005v1
2016 《StackGAN:Text to Photo realistic Image Synthesis with Stacked Generative Adversarial Network》- Han Zhang,arXiv:1612.03242
2017 《NIPS 2016 Tutorial: Generative Adversarial Networks 》- Ian Goodfellow , arXiv:1701.00160
2017 LS-GAN《 Loss-Sensitive Generative Adversarial Networks onLipschitz Densities》- Guo-Jun Qi ，arXiv:1701.06264
2017 WGAN 《Wasserstein GAN》- Martin Arjovsky ,arXiv:1701.07875v1
2017《Maximum-Likelihood Augmented Discrete Generative Adversarial Networks》-Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio，arXiv:1702.07983v1
2017《Boundary-Seeking Generative Adversarial Networks》- R Devon Hjelm, Athul Paul Jacob, Tong Che, Kyunghyun Cho, Yoshua Bengio ,arXiv:1702.08431
2017《Mode Regularized Generative Adversarial Networks》- Tong Che, Yanran Li, Athul Paul Jacob, Yoshua Bengio, Wenjie Li， ICLR 2017
2017《 Adversarial examples for generative models》- Jernej Kos, Ian Fischer, Dawn Song ， arXiv:1702.06832
2017《 Learning to Draw Dynamic Agent Goals with Generative Adversarial Networks》- Shariq Iqbal, John Pearson ，arXiv:1702.07319
2017 《WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images》- Jie Li, Katherine A. Skinner, Ryan M. Eustice, Matthew Johnson-Roberson ，arXiv:1702.07392
2017《Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning》- Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz ，arXiv:1702.07464
2017 《Generative Adversarial Active Learning》- Jia-Jie Zhu, José Bento ，arXiv:1702.07956
2017 《Maximum-Likelihood Augmented Discrete Generative Adversarial Networks》
- Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio ， arXiv:1702.07983
2017 《 Adversarial Networks for the Detection of Aggressive Prostate Cancer》- Simon Kohl, David Bonekamp, arXiv:1702.08014
2017《McGan: Mean and Covariance Feature Matching GAN》- Youssef Mroueh, Tom Sercu, Vaibhava Goel ，arXiv:1702.08398
2017 《 Age Progression/Regression by Conditional Adversarial Autoencoder》- Zhifei Zhang, Yang Song, Hairong Qi ，arXiv:1702.08423
2017 《ste-GAN-ography: Generating Steganographic Images via Adversarial Training 》- Jamie Hayes, George Danezis， arXiv:1703.00371
2017 《Generalization and Equilibrium in Generative Adversarial Nets (GANs) 》- Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, Yi Zhang， arXiv:1703.00573