• (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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  • 原文地址:https://www.cnblogs.com/wangxiaocvpr/p/6282315.html
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