TL;DR本文提出了一种基于特征函数距离(Characteristic Function Distance)的新颖的隐式生成模型评价方法,可用于无监督图像生成,并在实验中表现出优于WGAN和MMD-GAN变体的性能。
Abstract
In this paper, we formulate the problem of learning an Implicit Generative Model (IGM) as minimizing the expected distance between characteristic functions. Specifically, we match the characteristic functions of the real and generated data distributions under a suitably-chosen weighting distribution. This distance measure, which we term as the