X-Ray Sobolev Variational Auto-Encoders
Turinici, Gabriel (2019), X-Ray Sobolev Variational Auto-Encoders. https://basepub.dauphine.fr/handle/123456789/20349
TypeDocument de travail / Working paper
External document linkhttps://hal.archives-ouvertes.fr/hal-02387084
Cahier de recherche CEREMADE, Université Paris-Dauphine
Series titleCahier de recherche CEREMADE, Université Paris-Dauphine
MetadataShow full item record
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)The quality of the generative models (Generative adversarial networks, Variational Auto-Encoders, ...) depends heavily on the choice of a good probability distance. However some popular metrics lack convenient properties such as (geodesic) convexity, fast evaluation and so on. To address these shortcomings, we introduce a class of distances that have built-in convexity. We investigate the relationship with some known paradigms (sliced distances, reproducing kernel Hilbert spaces, energy distances). The distances are shown to posses fast implementations and are included in an adapted Variational Auto-Encoder termed X-ray Sobolev Variational Auto-Encoder (XS-VAE) which produces good quality results on standard generative datasets.
Subjects / Keywordsmachine learning; variational auto-encoder; neural network; deep learning
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