Generative Adversarial Net 生成对抗模型
资料搜集 论文＋代码：
1. 纹理合成 Markovian Generative Adversarial Networks (MGANs) for training generative neural networks for efficient texture synthesis
简介：Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks Chuan Li, Michael Wand This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative neural networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images)
dependencies: caffe
github:https://github.com/chuanli11/MGANs
2. Generative Adversarial Nets :
生成对抗网络的提出之作。
dependencies: Pylearn2
github:http://www.github.com/goodfeli/adversarial
3. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
简介：2016年ICLR，文章基于GAN提出一个中无监督的CNN网络，取名为DCGANs，采取一系列的约束使得训练模型更加稳定和可控。利用训练得到的判别网络D，可以实现image分类任务，允许操作语义的特性生成样本（allowing for easymanipulation of many semantic qualities of generated samples.）
dependencies: TORCH Chainer TensorFlow 等
github: https://github.com/Newmu/dcgan_code
4. Generative moment matching networks
github:https://github.com/yujiali/gmmn
5. Eyescream: Deep Generative Image Models (Laplacian Pyramid of Adversarial Networks)
github:https://github.com/facebook/eyescream
6. Generative Image Modeling using Style and Structure Adversarial Networks
7. Generating Images from Captions with Attention
github:https://github.com/emansim/text2image
8. InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
github：https://github.com/openai/InfoGAN
将信息论引入了GAN中，出发点是，既然 GAN 的自由度是由于仅有一个 noise z，而无法控制 GAN 如何利用这个 z。那么我们就尽量去想办法在 “如何利用 z” 上做文章。于是将 z 做了拆解，认为 GAN 中生成模型（G）应该包含的 “先验” 分成两种： （1）不能再做压缩的 noise z；（2）和可解释地、有隐含意义的一组隐变量 c_1, c_2, …, c_L，简写为 c ，利用了互信息的建模方式，即 c 应该和生成模型 （G）基于 z 和 c 生成的图片，即 G ( z,c )，高度相关 —— 互信息大。其次，他们利用 c 的天然特性，控制 c 的维度，使得 infoGAN 能控制生成的图片在某一个特定语义维度的变化
博客：
1.http://mp.weixin.qq.com/s?__biz=MzI1NTE4NTUwOQ==&mid=2650325352&idx=1&sn=90fb15cee44fa7175a804418259d352e&scene=1&srcid=0829SajiSHPncSQ6mMpGfVJ0#wechat_redirect
2.http://m.blog.csdn.net/article/details?id=52338052
3.http://blog.csdn.net/solomon1558/article/details/52338052
4.https://zhuanlan.zhihu.com/p/21341440
5.https://zhuanlan.zhihu.com/p/20924929
6.http://mp.weixin.qq.com/s?__biz=MzI3MTA0MTk1MA==&mid=2651985602&idx=1&sn=80482a460704f300efe914e07acdc39e&scene=0#wechat_redirect
7.http://it.sohu.com/20160814/n464137514.shtml