【GAN货】生成对抗网络知识资料全集(论文/代码/教程/视频/文章等)

Energy-Based GANs 以及Yann Le Cun 的相关研究

http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf

模式正则化GAN

https://arxiv.org/pdf/1612.02136.pdf

最新NIPS2016也有最新的关于训练GAN模型的总结

https://github.com/soumith/ganhacks

The GAN Zoo千奇百怪的生成对抗网络，都在这里了。你没看错，里面已经有有近百个了。

https://github.com/hindupuravinash/the-gan-zoo

Ian Goodfellow的GANs报告ICCV 2017的中文讲稿

https://mp.weixin.qq.com/s/nPBFrnO3_QJjAzm37G5ceQ

Ian Goodfellow的GANs报告NIPS 2016

http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf

Ian Goodfellow的GANs报告NIPS 2016 的中文讲稿

http://www.sohu.com/a/121189842_465975

Russ Salakhutdinov的深度生成模型

http://www.cs.toronto.edu/~rsalakhu/talk_Montreal_2016_Salakhutdinov.pdf

训练GANs的技巧和窍门

https://github.com/soumith/ganhacks

OpenAI生成模型

https://blog.openai.com/generative-models/

用Keras实现MNIST生成对抗模型

https://oshearesearch.com/index.PHP/2016/07/01/mnist-generative-adversarial-model-in-keras/

用深度学习TensorFlow实现图像修复

http://bamos.github.io/2016/08/09/deep-completion/

深入浅出：GAN原理与应用入门介绍

https://zhuanlan.zhihu.com/p/28731033

港理工在读博士李嫣然深入浅出GAN之应用篇

https://pan.baidu.com/s/1o8n4UDk 密码: 78wt

中科院自动化所 中文综述 《生成式对抗网络 GAN 的研究进展与展望》

https://pan.baidu.com/s/1dEMITo9 密码: qqcc

萌物生成器：如何使用四种GAN制造猫图

https://zhuanlan.zhihu.com/p/27769807

GAN学习指南：从原理入门到制作生成Demo

https://zhuanlan.zhihu.com/p/24767059x

生成式对抗网络GAN研究进展

http://blog.csdn.net/solomon1558/article/details/52537114

生成对抗网络（GAN）的前沿进展（论文、报告、框架和Github资源）汇总

http://blog.csdn.net/love666666shen/article/details/74953970

TensorFlow实现深度卷积生成对抗模型（DCGAN）

https://github.com/carpedm20/DCGAN-tensorflow

Torch实现深度卷积生成对抗模型（DCGAN）

https://github.com/soumith/dcgan.torch

Keras实现深度卷积生成对抗模型（DCGAN）

https://github.com/jacobgil/keras-dcgan

使用神经网络生成自然图像（Facebook的Eyescream项目）

https://github.com/facebook/eyescream

对抗自编码（AdversarialAutoEncoder）

https://github.com/musyoku/adversarial-autoencoder

利用ThoughtVectors 实现文本到图像的合成

https://github.com/paarthneekhara/text-to-image

对抗样本生成器（Adversarialexample generator）

https://github.com/e-lab/torch-toolbox/tree/master/Adversarial

深度生成模型的半监督学习

https://github.com/dpkingma/nips14-ssl

GANs的训练方法

https://github.com/openai/improved-gan

生成式矩匹配网络（Generative Moment Matching Networks, GMMNs）

https://github.com/yujiali/gmmn

对抗视频生成

https://github.com/dyelax/Adversarial_Video_Generation

基于条件对抗网络的图像到图像翻译（pix2pix）

https://github.com/phillipi/pix2pix

对抗机器学习库Cleverhans

https://github.com/openai/cleverhans

对抗实例的解释和利用（Explaining and Harnessing Adversarial Examples）2014

https://arxiv.org/pdf/1412.6572.pdf

基于深度生成模型的半监督学习（ Semi-Supervised Learning with Deep Generative Models ）2014

https://arxiv.org/pdf/1406.5298v2.pdf

基于拉普拉斯金字塔生成式对抗网络的深度图像生成模型（Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks）2015

http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-5.

http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-5.

https://arxiv.org/pdf/1606.03498v1.pdf

条件生成对抗网络（Conditional Generative Adversarial Nets）2014

https://arxiv.org/pdf/1411.1784v1.pdf

生成式矩匹配网络（Generative Moment Matching Networks）2015

http://proceedings.mlr.press/v37/li15.pdf

超越均方误差的深度多尺度视频预测（Deep multi-scale video prediction beyond mean square error）2015

https://arxiv.org/pdf/1511.05440.pdf

通过学习相似性度量的超像素自编码（Autoencoding beyond pixels using a learned similarity metric）2015

https://arxiv.org/pdf/1512.09300.pdf

对抗自编码（Adversarial Autoencoders）2015

https://arxiv.org/pdf/1511.05644.pdf

InfoGAN:基于信息最大化GANs的可解释表达学习（InfoGAN:Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets）2016

https://arxiv.org/pdf/1606.03657v1.pdf

上下文像素编码：通过修复进行特征学习（Context Encoders: Feature Learning by Inpainting）2016

http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf

http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf

http://proceedings.mlr.press/v48/reed16.pdf

基于像素卷积神经网络的条件生成图片（Conditional Image Generation with PixelCNN Decoders）2015

https://arxiv.org/pdf/1606.05328.pdf

对抗特征学习（Adversarial Feature Learning）2016

https://arxiv.org/pdf/1605.09782.pdf

结合逆自回归流的变分推理（Improving Variational Inference with Inverse Autoregressive Flow ）2016

https://papers.nips.cc/paper/6581-improving-variational-autoencoders-with-inverse-autoregressive-flow.pdf

深度学习系统对抗样本黑盒攻击（Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples）2016

https://arxiv.org/pdf/1602.02697.pdf

参加，推断，重复：基于生成模型的快速场景理解（Attend, infer, repeat: Fast scene understanding with generative models）2016

https://arxiv.org/pdf/1603.08575.pdf

f-GAN: 使用变分散度最小化训练生成神经采样器（f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization ）2016

http://papers.nips.cc/paper/6066-tagger-deep-unsupervised-perceptual-grouping.pdf

在自然图像流形上的生成视觉操作（Generative Visual Manipulation on the Natural Image Manifold）2016

https://arxiv.org/pdf/1609.03552.pdf

通过平均差异最大优化训练生成神经网络（Training generative neural networks via Maximum Mean Discrepancy optimization）2015

https://arxiv.org/pdf/1505.03906.pdf

对抗性推断学习（Adversarially Learned Inference）2016

https://arxiv.org/pdf/1606.00704.pdf

基于循环对抗网络的图像生成（Generating images with recurrent adversarial networks）2016

https://arxiv.org/pdf/1602.05110.pdf

生成对抗模仿学习(Generative Adversarial Imitation Learning）2016

http://papers.nips.cc/paper/6391-generative-adversarial-imitation-learning.pdf

基于3D生成对抗模型学习物体形状的概率隐空间（Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling）2016

https://arxiv.org/pdf/1610.07584.pdf

学习画画（Learning What and Where to Draw）2016

https://arxiv.org/pdf/1610.02454v1.pdf

基于辅助分类器GANs的条件图像合成（Conditional Image Synthesis with Auxiliary Classifier GANs）2016

https://arxiv.org/pdf/1610.09585.pdf

隐生成模型的学习（Learning in Implicit Generative Models）2016

https://arxiv.org/pdf/1610.03483.pdf

VIME: 变分信息最大化探索（VIME: Variational Information Maximizing Exploration）2016

http://papers.nips.cc/paper/6591-vime-variational-information-maximizing-exploration.pdf

生成对抗网络的展开（Unrolled Generative Adversarial Networks）2016

https://arxiv.org/pdf/1611.02163.pdf

训练生成对抗网络的基本方法（Towards Principled Methods for Training Generative Adversarial Networks）2017

https://arxiv.org/pdf/1701.04862.pdf

基于内省对抗网络的神经图像编辑（Neural Photo Editing with Introspective Adversarial Networks）2016

https://arxiv.org/pdf/1609.07093.pdf

基于解码器的生成模型的定量分析（On the Quantitative Analysis of Decoder-Based Generative Models ）2016

https://arxiv.org/pdf/1611.04273.pdf

结合生成对抗网络和Actor-Critic 方法（Connecting Generative Adversarial Networks and Actor-Critic Methods）2016

https://arxiv.org/pdf/1610.01945.pdf

通过对抗网络使用模拟和非监督图像训练（ Learning from Simulated and Unsupervised Images through Adversarial Training）2016

https://arxiv.org/pdf/1612.07828.pdf

基于上下文RNN-GANs的抽象推理图的生成（Contextual RNN-GANs for Abstract Reasoning Diagram Generation）2016

https://arxiv.org/pdf/1609.09444.pdf

生成多对抗网络（Generative Multi-Adversarial Networks）2016

https://arxiv.org/pdf/1611.01673.pdf

生成对抗网络组合（Ensembles of Generative Adversarial Network）2016

https://arxiv.org/pdf/1612.00991.pdf

改进生成器目标的GANs(Improved generator objectives for GANs) 2016

https://arxiv.org/pdf/1612.02780.pdf

生成对抗模型的隐向量精准修复（Precise Recovery of Latent Vectors from Generative Adversarial Networks）2017

https://openreview.NET/pdf?id=HJC88BzFl

生成混合模型（Generative Mixture of Networks）2017

https://arxiv.org/pdf/1702.03307.pdf

记忆生成时空模型（Generative Temporal Models with Memory）2017

https://arxiv.org/pdf/1702.04649.pdf

停止GAN暴力：生成性非对抗模型（Stopping GAN Violence: Generative Unadversarial Networks）2017

https://arxiv.org/pdf/1703.02528.pdf