Rethinking Disentanglement under Dependent Factors of Variation Article Swipe

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Antonio Almudévar , Alfonso Ortega , Luis Vicente , Antonio Miguel , Eduardo Lleida ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2408.07016

Representation learning is an approach that allows to discover and extract the factors of variation from the data. Intuitively, a representation is said to be disentangled if it separates the different factors of variation in a way that is understandable to humans. Definitions of disentanglement and metrics to measure it usually assume that the factors of variation are independent of each other. However, this is generally false in the real world, which limits the use of these definitions and metrics to very specific and unrealistic scenarios. In this paper we give a definition of disentanglement based on information theory that is also valid when the factors of variation are not independent. Furthermore, we relate this definition to the Information Bottleneck Method. Finally, we propose a method to measure the degree of disentanglement from the given definition that works when the factors of variation are not independent. We show through different experiments that the method proposed in this paper correctly measures disentanglement with non-independent factors of variation, while other methods fail in this scenario.

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Variation (astronomy) Statistics Econometrics Mathematics Physics Astrophysics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2408.07016
PDF: https://arxiv.org/pdf/2408.07016
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4402427394

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DOI: https://doi.org/10.48550/arxiv.2408.07016

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Title: Rethinking Disentanglement under Dependent Factors of Variation

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-08-13

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Authors: Antonio Almudévar, Alfonso Ortega, Luis Vicente, Antonio Miguel, Eduardo Lleida

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Landing page: https://arxiv.org/abs/2408.07016

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PDF URL: https://arxiv.org/pdf/2408.07016

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Concepts: Variation (astronomy), Statistics, Econometrics, Mathematics, Physics, Astrophysics

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Cited by: 0

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Related works (count): 10

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