%0 Journal Article
%T Sliced Generative Models
%A Knop, Szymon
%A Mazur, Marcin
%A Tabor, Jacek
%A Podolak, Igor T.
%A Spurek, Przemysław
%J Schedae Informaticae
%V 2018
%R 10.4467/20838476SI.18.006.10411
%N Volume 27
%P 69-79
%K Generative model, AutoEncoder, Wasserstein distances
%@ 1732-3916
%D 2018
%U https://ejournals.eu/en/journal/schedae-informaticae/article/sliced-generative-models
%X 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 modification 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 Frechet Inception Distance (FID).