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Cramer-Wold Auto-Encoder

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Cramer-Wold Auto-Encoder

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dc.contributor.author Knop, Szymon [USOS112545] pl
dc.contributor.author Spurek, Przemysław [SAP14004607] pl
dc.contributor.author Tabor, Jacek [SAP11017416] pl
dc.contributor.author Podolak, Igor [SAP11012911] pl
dc.contributor.author Mazur, Marcin [SAP11017645] pl
dc.contributor.author Jastrzębski, Stanisław [USOS147562] pl
dc.date.accessioned 2020-10-29T19:05:49Z
dc.date.available 2020-10-29T19:05:49Z
dc.date.issued 2020 pl
dc.identifier.issn 1532-4435 pl
dc.identifier.uri https://ruj.uj.edu.pl/xmlui/handle/item/251876
dc.language eng pl
dc.rights Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/pl/legalcode *
dc.title Cramer-Wold Auto-Encoder pl
dc.type JournalArticle pl
dc.description.physical 1-28 pl
dc.identifier.weblink http://jmlr.org/papers/v21/19-560.html pl
dc.abstract.en The computation of the distance to the true distribution is a key component of most state-of-the-art generative models. Inspired by prior works on the Sliced-Wasserstein Auto-Encoders(SWAE) and the Wasserstein Auto-Encoders with MMD-based penalty (WAE-MMD), wepropose a new generative model – a Cramer-Wold Auto-Encoder (CWAE). A fundamentalcomponent of CWAE is the characteristic kernel, the construction of which is one of the goalsof this paper, from here on referred to as the Cramer-Wold kernel. Its main distinguishingfeature is that it has a closed-form of the kernel product of radial Gaussians. Consequently,CWAE model has a closed-form for the distance between the posterior and the normal prior,which simplifies the optimization procedure by removing the need to sample in order tocompute the loss function. At the same time, CWAE performance often improves uponWAE-MMD and SWAE on standard benchmarks.Keywords:Auto-Encoder, Generative model, Wasserstein Auto-Encoder, Cramer-WoldTheorem, Deep neural network pl
dc.description.volume 21 pl
dc.identifier.eissn 1533-7928 pl
dc.title.journal Journal of Machine Learning Research pl
dc.language.container eng pl
dc.date.accession 2020-10-27 pl
dc.affiliation Wydział Matematyki i Informatyki pl
dc.affiliation Wydział Matematyki i Informatyki : Instytut Informatyki Analitycznej pl
dc.affiliation Wydział Matematyki i Informatyki : Instytut Informatyki i Matematyki Komputerowej pl
dc.subtype Article pl
dc.rights.original CC-BY; otwarte czasopismo; ostateczna wersja wydawcy; w momencie opublikowania; 0 pl
dc.identifier.project 2019/33/B/ST6/00894 pl
dc.identifier.project 2017/25/B/ST6/01271 pl
dc.identifier.project POIR.04.04.00-00-14DE/18-00 pl
dc.identifier.project ROD UJ / OP pl
.pointsMNiSW [2020 A]: 140


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Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa Except where otherwise noted, this item's license is described as Udzielam licencji. Uznanie autorstwa 4.0 Międzynarodowa