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Disentanglement by nonlinear ICA with General Incompressible-flow Networks (GIN)

A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process. Recent breakthrough work by Khemakhem et al. (2019) on nonlinear ICA has answered this question for a broad class of conditi...

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Hauptverfasser: Sorrenson, Peter (VerfasserIn) , Rother, Carsten (VerfasserIn) , Köthe, Ullrich (VerfasserIn)
Dokumenttyp: Article (Journal) Kapitel/Artikel
Sprache:Englisch
Veröffentlicht: 14 Jan 2020
In: Arxiv
Year: 2020, Pages: 1-23
DOI:10.48550/arXiv.2001.04872
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2001.04872
Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2001.04872
Volltext
Verfasserangaben:Peter Sorrenson, Carsten Rother, Ullrich Köthe

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