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Learning disentangled discrete representations
Friede, David
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Reimers, Christian
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Stuckenschmidt, Heiner
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Niepert, Mathias
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DOI:
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https://doi.org/10.1007/978-3-031-43421-1_35
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URL:
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https://link.springer.com/chapter/10.1007/978-3-03...
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Document Type:
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Conference or workshop publication
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Year of publication:
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2023
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Book title:
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Machine learning and knowledge discovery in databases : research track : European conference, EMCL PKDD 2023, Turin, Italy, september 18-22,2023, proceedings, part IV
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The title of a journal, publication series:
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Lecture Notes in Computer Science
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Volume:
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14172
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Page range:
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593-609
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Conference title:
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ECML PKDD 2023, European Conference on Machine Learning and Data Mining
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Location of the conference venue:
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Torino, Italy
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Date of the conference:
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18.-22.09.2023
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Publisher:
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Koutra, Danai
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Plant, Claudia
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Gomez Rodriguez, Manuel
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Baralis, Elena
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Bonchi, Francesco
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Place of publication:
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Berlin [u.a.]
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Publishing house:
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Springer
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ISBN:
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978-3-031-43420-4 , 978-3-031-43421-1
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ISSN:
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0302-9743 , 1611-3349
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Subject:
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004 Computer science, internet
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Keywords (English):
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categorical VAE , disentanglement
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Abstract:
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Recent successes in image generation, model-based reinforcement learning, and text-to-image generation have demonstrated the empirical advantages of discrete latent representations, although the reasons behind their benefits remain unclear. We explore the relationship between discrete latent spaces and disentangled representations by replacing the standard Gaussian variational autoencoder (VAE) with a tailored categorical variational autoencoder. We show that the underlying grid structure of categorical distributions mitigates the problem of rotational invariance associated with multivariate Gaussian distributions, acting as an efficient inductive prior for disentangled representations. We provide both analytical and empirical findings that demonstrate the advantages of discrete VAEs for learning disentangled representations. Furthermore, we introduce the first unsupervised model selection strategy that favors disentangled representations.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Friede, David
;
Reimers, Christian
;
Stuckenschmidt, Heiner
;
Niepert, Mathias
Google Scholar:
Friede, David
;
Reimers, Christian
;
Stuckenschmidt, Heiner
;
Niepert, Mathias
ORCID:
Friede, David ; Reimers, Christian ; Stuckenschmidt, Heiner  ORCID: 0000-0002-0209-3859 ; Niepert, Mathias
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