论文原文：Generative Adversarial Nets (neurips.cc)
李沐GAN论文逐段精读：GAN论文逐段精读【论文精读】_哔哩哔哩_bilibili
论文代码：http://www.github.com/goodfeli/adversarial
Ian, J. et al. (2014) 'Generative adversarial network', NIPS'14: Proceedings of the 27th International Conference on Neural Information Processing Systems , Vol. 2, pp 2672--2680. doi: https://doi.org/10.48550/arXiv.1406.2661
未完待续
- GAN
1.1. 整体实现步骤
1.2. GAN理念
1.3. GAN弊端和局限
2. GAN论文原文学习
2.1. Abstract
①They combined generative model 
and discriminative model 
together, which forms a new model. is the "cheating" part which focus on imitating and is the "distinguishing" part which focus on distinguishing where the data comes from.
②This model is rely on a "minmax" function
③GAN does not need Markov chains or unrolled approximate inference nets
④They designed qualitative and quantitative evaluation to analyse the feasibility of GAN
2.2. Introduction
①The authors praised deep learning and briefly mentioned its prospects
②Due to the difficulty of fitting or approximating the distribution of the ground truth, the designed a new generative model
③They compare the generated model to the person who makes counterfeit money, and the discriminative model to the police. Both parties will mutually promote and grow. The authors ultimately hope that the ability of the counterfeiter can be indistinguishable from the genuine product
④Both and are MLP, and passes random noise
⑤They just adopt backpropagation and dropout in training
corpora 全集；corpus 的复数
counterfeiter n.伪造者;制假者;仿造者
2.3. Related work
①Recent works are concentrated on approximating function, such as succesful deep Boltzmann machine. However, their likelihood functions are too complex to process.
②Therefore, here comes generative model, which only generates samples but does not approximates function. Generative stochastic networks are an classic generative model.
③Their backpropagation:
④Variational autoencoders (VAEs) in Kingma and Welling and Rezende et al. do the similar work. However, VAEs are modeled by differentiate hidden units, which is contrary to GANs.
2.4. Adversarial nets
2.5. Theoretical Results
2.5.1. Global Optimality of p_g = p_data
2.5.2. Convergence of Algorithm 1
2.6. Experiments
2.7. Advantages and disadvantages
2.8. Conclusions and future work
- 知识补充
3.1. 散度
（1）KL散度
（2）JS散度