虚构 GAN：使用历史模型训练 GANs

Mar, 2018

Fictitious GAN: Training GANs with Historical Models

Hao Ge, Yin Xia, Xu Chen, Randall Berry, Ying Wu

TL;DR通过博弈论视角，使用历史混合模型对深度神经网络进行训练的Fictitious GAN方法可以有效地解决收敛问题，从而使生成器的输出分布在渐进意义下收敛于数据样本的分布。

Abstract

generative adversarial networks (GANs) are powerful tools for learning generative models. In practice, the training may suffer from lack of convergence. GANs are commonly viewed as a two-player zero-sum game betw