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arXiv · 1901.10417

Sliced generative models

Szymon Knop·Marcin Mazur·Jacek Tabor·Igor Podolak·Przemysław Spurek
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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).

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Szymon Knop, Marcin Mazur, Jacek Tabor, Igor Podolak, Przemysław Spurek. 2019-01-29. Sliced generative models. https://arxiv.org/abs/1901.10417

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