Sliced Generative Models
Szymon Knop, Marcin Mazur, Jacek Tabor, Igor T. Podolak, Przemysław Spurek · Schedae Informaticae · 2018
In this paper we discuss a class of AutoEncoder based generative models based on one dimensional sliced approach.The idea is based on the reduction of the discrimination between samples to one-dimensional case.Our experiments show that methods can be divided into two groups.First consists of methods which are a modication of standard normality tests, while the second is based on classical distances between samples.It turns out that both groups are correct generative models, but the second one gives a slightly faster decrease rate of Fréchet Inception Distance (FID).