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Enriching Disentanglement: From Logical Definitions to Quantitative Metrics
May 22, 2024, 4:43 a.m. | Yivan Zhang, Masashi Sugiyama
cs.LG updates on arXiv.org arxiv.org
Abstract: Disentangling the explanatory factors in complex data is a promising approach for generalizable and data-efficient representation learning. While a variety of quantitative metrics for learning and evaluating disentangled representations have been proposed, it remains unclear what properties these metrics truly quantify. In this work, we establish a theoretical connection between logical definitions of disentanglement and quantitative metrics using topos theory and enriched category theory. We introduce a systematic approach for converting a first-order predicate into …
abstract arxiv cs.lg data definitions metrics quantitative replace representation representation learning type while work
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