Desiderata for Representation Learning from Identifiability, Disentanglement, and Group-Structuredness
Hamza Keurti · Patrik Reizinger · Bernhard Schölkopf · Wieland Brendel
2023 Poster
in
Workshop: 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)
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Workshop: 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML)
Abstract
Machine learning subfields define useful representations differently: disentanglement strives for semantic meaning and symmetries, identifiability for recovering the ground-truth factors of the (unobservable) data generating process, group-structured representations for equivariance. We demonstrate that despite their merits, each approach has shortcomings. Surprisingly, joining forces helps overcome the limitations: we use insights from latent space statistics, geometry, and topology in our examples to elucidate how combining the desiderata of identifiability, disentanglement, and group structure yields more useful representations.
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