Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning

A central problem in unsupervised deep learning is how to find useful representations of high-dimensional data, sometimes called “disentanglement.” Most approaches are heuristic and lack a proper theoretical foundation. In linear representation learning, independent component analysis (ICA) has been...

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Detalles Bibliográficos
Autores principales: Hyvärinen, Aapo, Khemakhem, Ilyes, Morioka, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591132/
https://www.ncbi.nlm.nih.gov/pubmed/37876900
http://dx.doi.org/10.1016/j.patter.2023.100844

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591132/
https://www.ncbi.nlm.nih.gov/pubmed/37876900
http://dx.doi.org/10.1016/j.patter.2023.100844

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