Name
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). |
Year | Venue | DocType |
|---|---|---|
2019 | CoRR | Journal |
Volume | Citations | PageRank |
abs/1901.10417 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Szymon Knop | 1 | 0 | 1.01 |
| Marcin Mazur | 2 | 0 | 3.72 |
| Jacek Tabor | 3 | 72 | 26.52 |
| Igor T. Podolak | 4 | 55 | 8.61 |
| Przemyslaw Spurek | 5 | 33 | 13.00 |