Sliced generative models

Szymon Knop; Marcin Mazur; Jacek Tabor; Igor Podolak; Przemysław Spurek

Sliced generative models

Machine Learning 2019-01-30 v1 Machine Learning

Authors: Szymon Knop , Marcin Mazur , Jacek Tabor , Igor Podolak , Przemysław Spurek

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 Fr\'{e}chet Inception Distance (FID).

Keywords

randomized algorithm

Cite

@article{arxiv.1901.10417,
  title  = {Sliced generative models},
  author = {Szymon Knop and Marcin Mazur and Jacek Tabor and Igor Podolak and Przemysław Spurek},
  journal= {arXiv preprint arXiv:1901.10417},
  year   = {2019}
}

Comments

11 pages, 4 figures, conference

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