TY - JOUR
TI - Sliced Generative Models
AU - Knop, Szymon
AU - Mazur, Marcin
AU - Tabor, Jacek
AU - Podolak, Igor T.
AU - Spurek, Przemysław
TI - Sliced Generative Models
AB - 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).
VL - 2018
IS - Volume 27
PY - 2018
SN - 1732-3916
C1 - 2083-8476
SP - 69
EP - 79
DO - 10.4467/20838476SI.18.006.10411
UR - https://ejournals.eu/en/journal/schedae-informaticae/article/sliced-generative-models
KW - Generative model
KW - AutoEncoder
KW - Wasserstein distances