PeiyaoWang / AdversarialNetsPapers

The classical papers and codes about generative adversarial nets

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AdversarialNetsPapers

The good papers about Generative Adversarial Networks

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]

[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]

[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)

[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]

Generation High-Quality Images

[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)(ICLR)

[Generative Adversarial Text to Image Synthesis] [Paper][Code][code]

[Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)

[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]

[Improved Training of Wasserstein GANs] [Paper][Code]

[Boundary Equibilibrium Generative Adversarial Networks Implementation in Tensorflow] [Paper][Code]

[Progressive Growing of GANs for Improved Quality, Stability, and Variation ] [Paper][Code]

Semi-supervised learning

[Adversarial Training Methods for Semi-Supervised Text Classification] [Paper][Note]( Ian Goodfellow Paper)

[Improved Techniques for Training GANs] [Paper][Code](Goodfellow's paper)

[Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] [Paper](ICLR)

[Semi-Supervised QA with Generative Domain-Adaptive Nets] [Paper](ACL 2017)

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 and Generation

[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]

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]

[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]

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](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]

[Generalization and Equilibrium in Generative Adversarial Nets] [Paper](ICML 2017)

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][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)

[GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data ] [Paper](BMVC 2017)[code]

[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]

Improving Classification And Recong

[Generative OpenMax for Multi-Class Open Set Classification] [Paper](BMVC 2017)

[Controllable Invariance through Adversarial Feature Learning] [Paper][code](NIPS 2017)

[Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro] [Paper][Code] (ICCV2017)

[Learning from Simulated and Unsupervised Images through Adversarial Training] [Paper][code](Apple paper, CVPR 2017 Best 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

Tutorial

[1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]

[2] [PDF](NIPS Lecun Slides)

[3] [ICCV 2017 Tutorial About GANS]

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The classical papers and codes about generative adversarial nets