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Abstrakty
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 Fréchet Inception Distance (FID).
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
69--79
Opis fizyczny
Bibliogr. 11 poz., rys.
Twórcy
autor
- Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
autor
- Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
autor
- Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
autor
- Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
autor
- Faculty of Mathematics and Computer Science Jagiellonian University, Łojasiewicza 6, 30-348 Kraków, Poland
Bibliografia
- [1] H. Cramér and H. Wold. Some theorems on distribution functions. London Math.Soc., 11:290-294, 1936.
- [2] M. Hazewinkel, ed. Kolmogorov-Smirnov test. Encyclopedia of Mathematics. Springer Science+Business Media B.V. / Kluwer Academic Publishers, 2001.
- [3] N. Henze. Invariant tests for multivariate normality: a critical review. Statist. Papers, 43(4):467 506, 2002.
- [4] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, G. Klambauer, and S. Hochreiter. Gans trained by a two time-scale update rule converge to a nash equilibrium. arXiv:1706.08500, 2017.
- [5] D.P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv:1312.6114, 2014.
- [6] S. Kolouri, P.E. Pope, C.E. Martin, and G.K. Rohde. Sliced wasserstein autoencoders. 2018.
- [7] M. Mazur and P. Kościelniak. On some goodness of fit tests for normality based on the optimal transport distance. submitted.
- [8] A. Palmer, D. Dey, and J. Bi. Reforming generative autoencoders via goodnessof-fit hypothesis testing. UAI, 2018.
- [9] B.W. Silverman. Density estimation for statistics and data analysis. Monographs on Statistics and Applied Probability. Chapman & Hall, London, 1986.
- [10] Jacek Tabor, Szymon Knop, Przemysªaw Spurek, Igor Podolak, Marcin Mazur, and Stanisªaw Jastrzębski. Cramer-wold autoencoder. arXiv preprint arXiv:1805.09235, 2018.
- [11] I. Tolstikhin, O. Bousquet, S. Gelly, and B. Schoelkopf. Wasserstein autoencoders. arXiv preprint arXiv:1711.01558, 2017.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
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