@article{88ab4f5a-8464-4c8f-8867-2f87add67c26,
author = {Szymon  Knop, Marcin Mazur, Jacek Tabor, Igor T. Podolak, Przemysław Spurek},
title = {Sliced Generative Models},
journal = {Schedae Informaticae},
volume = {2018},
number = {Volume 27},
year = {2018},
issn = {1732-3916},
pages = {69-79},keywords = {Generative model; AutoEncoder; Wasserstein distances},
abstract = {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).},
doi = {10.4467/20838476SI.18.006.10411},
url = {https://ejournals.eu/en/journal/schedae-informaticae/article/sliced-generative-models}
}