AdversarialNetsPapers&Code
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AdversarialNetsPapers
The classical Papers about adversarial nets
The First paper
:white_check_mark: [Generative Adversarial Nets] [Paper] [Code](the first paper about it)
Unclassified
:white_check_mark: [Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks] [Paper][Code]
:white_check_mark: [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks] [Paper][Code](Gan with convolutional networks)(ICLR)
:white_check_mark: [Adversarial Autoencoders] [Paper][Code]
:white_check_mark: [Generating Images with Perceptual Similarity Metrics based on Deep Networks] [Paper]
:white_check_mark: [Generating images with recurrent adversarial networks] [Paper][Code]
:white_check_mark: [Generative Visual Manipulation on the Natural Image Manifold] [Paper][Code]
:white_check_mark: [Generative Adversarial Text to Image Synthesis] [Paper][Code][code]
:white_check_mark: [Learning What and Where to Draw] [Paper][Code]
:white_check_mark: [Adversarial Training for Sketch Retrieval] [Paper]
:white_check_mark: [Generative Image Modeling using Style and Structure Adversarial Networks] [Paper][Code]
:white_check_mark: [Generative Adversarial Networks as Variational Training of Energy Based Models] [Paper](ICLR 2017)
:white_check_mark: [Adversarial Training Methods for Semi-Supervised Text Classification] [Paper][Note]( Ian Goodfellow Paper)
:white_check_mark: [Learning from Simulated and Unsupervised Images through Adversarial Training] [Paper][code](Apple paper, CVPR 2017 Best Paper )
:white_check_mark: [Synthesizing the preferred inputs for neurons in neural networks via deep generator networks] [Paper][Code]
:white_check_mark: [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks] [Paper][Code]
:white_check_mark: [Adversarial Feature Learning] [Paper]
Ensemble
:white_check_mark: [AdaGAN: Boosting Generative Models] [Paper][[Code]](Google Brain)
Clustering
:white_check_mark: [Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks] Paper
Image blending
:white_check_mark: [GP-GAN: Towards Realistic High-Resolution Image Blending] [Paper][Code]
Image Inpainting
:white_check_mark: [Semantic Image Inpainting with Perceptual and Contextual Losses] [Paper][Code](CVPR 2017)
:white_check_mark: [Context Encoders: Feature Learning by Inpainting] [Paper][Code]
:white_check_mark: [Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks] [Paper]
:white_check_mark: [Generative face completion] [Paper]code
:white_check_mark: [Globally and Locally Consistent Image Completion] [MainPAGE](SIGGRAPH 2017)
Joint Probability
:white_check_mark: [Adversarially Learned Inference][Paper][Code]
Super-Resolution
:white_check_mark: [Image super-resolution through deep learning ][Code](Just for face dataset)
:white_check_mark: [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network] [Paper][Code](Using Deep residual network)
:white_check_mark: [EnhanceGAN] [Docs][[Code]]
Disocclusion
:white_check_mark: [Robust LSTM-Autoencoders for Face De-Occlusion in the Wild] [Paper]
Semantic Segmentation
:white_check_mark: [Adversarial Deep Structural Networks for Mammographic Mass Segmentation] [Paper][Code]
:white_check_mark: [Semantic Segmentation using Adversarial Networks] [Paper](soumith’s paper)
Object Detection
:white_check_mark: [Perceptual generative adversarial networks for small object detection] [Paper](CVPR 2017)
:white_check_mark: [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection] [Paper]code
RNN
:white_check_mark: [C-RNN-GAN: Continuous recurrent neural networks with adversarial training] [Paper][Code]
Conditional adversarial
:white_check_mark: [Conditional Generative Adversarial Nets] [Paper][Code]
:white_check_mark: [InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets] [Paper][Code][Code]
:white_check_mark: [Conditional Image Synthesis With Auxiliary Classifier GANs] [Paper][Code](GoogleBrain ICLR 2017)
:white_check_mark: [Pixel-Level Domain Transfer] [Paper][Code]
:white_check_mark: [Invertible Conditional GANs for image editing] [Paper][Code]
:white_check_mark: [Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space] [Paper][Code]
:white_check_mark: [StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks] [Paper][Code]
Video Prediction
:white_check_mark: [Deep multi-scale video prediction beyond mean square error] [Paper][Code](Yann LeCun’s paper)
:white_check_mark: [Generating Videos with Scene Dynamics] [Paper][Web][Code]
Texture Synthesis & style transfer
:white_check_mark: [Precomputed real-time texture synthesis with markovian generative adversarial networks] [Paper][Code](ECCV 2016)
Image translation
:white_check_mark: [UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION] [Paper][Code]
:white_check_mark: [Image-to-image translation using conditional adversarial nets] [Paper][Code][Code]
:white_check_mark: [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks] [Paper][Code]
:white_check_mark: [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks] [Paper][Code]
:white_check_mark: [Unsupervised Image-to-Image Translation with Generative Adversarial Networks] [Paper]
:white_check_mark: [Unsupervised Image-to-Image Translation Networks] [Paper]
GAN Theory
:white_check_mark: [Energy-based generative adversarial network] [Paper][Code](Lecun paper)
:white_check_mark: [Improved Techniques for Training GANs] [Paper][Code](Goodfellow’s paper)
:white_check_mark: [Mode Regularized Generative Adversarial Networks] [Paper](Yoshua Bengio , ICLR 2017)
:white_check_mark: [Improving Generative Adversarial Networks with Denoising Feature Matching] [Paper][Code](Yoshua Bengio , ICLR 2017)
:white_check_mark: [Sampling Generative Networks] [Paper][Code]
:white_check_mark: [How to train Gans] [Docu]
:white_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] [Paper](ICLR 2017)
:white_check_mark: [Unrolled Generative Adversarial Networks] [Paper][Code](ICLR 2017)
:white_check_mark: [Least Squares Generative Adversarial Networks] [Paper][Code]
:white_check_mark: [Wasserstein GAN] [Paper][Code]
:white_check_mark: [Improved Training of Wasserstein GANs] [Paper][Code](The improve of wgan)
:white_check_mark: [Towards Principled Methods for Training Generative Adversarial Networks] [Paper]
3D
:white_check_mark: [Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling] [Paper][Web][code](2016 NIPS)
:white_check_mark: [Transformation-Grounded Image Generation Network for Novel 3D View Synthesis] [Web](CVPR 2017)
MUSIC
:white_check_mark: [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation using 1D and 2D Conditions] [Paper][HOMEPAGE]
Face Generative and Editing
:white_check_mark: [Autoencoding beyond pixels using a learned similarity metric] [Paper][code]
:white_check_mark: [Coupled Generative Adversarial Networks] [Paper][Caffe Code][Tensorflow Code](NIPS)
:white_check_mark: [Invertible Conditional GANs for image editing] [Paper][Code]
:white_check_mark: [Learning Residual Images for Face Attribute Manipulation] [Paper][code](CVPR 2017)
:white_check_mark: [Neural Photo Editing with Introspective Adversarial Networks] [Paper][Code](ICLR 2017)
:white_check_mark: [Neural Face Editing with Intrinsic Image Disentangling] [Paper](CVPR 2017)
:white_check_mark: [Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis] [Paper](ICCV 2017)
For discrete distributions
:white_check_mark: [Maximum-Likelihood Augmented Discrete Generative Adversarial Networks] [Paper]
:white_check_mark: [Boundary-Seeking Generative Adversarial Networks] [Paper]
:white_check_mark: [GANS for Sequences of Discrete Elements with the Gumbel-softmax Distribution] [Paper]
Adversarial Examples
:white_check_mark: [SafetyNet: Detecting and Rejecting Adversarial Examples Robustly] [Paper]
Project
:white_check_mark: [cleverhans] [Code](A library for benchmarking vulnerability to adversarial examples)
:white_check_mark: [reset-cppn-gan-tensorflow] [Code](Using Residual Generative Adversarial Networks and Variational Auto-encoder techniques to produce high resolution images)
:white_check_mark: [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
:white_check_mark: [1] http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf (NIPS Goodfellow Slides)[Chinese Trans][details]
:white_check_mark: [2] [PDF](NIPS Lecun Slides)
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