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How to Not Measure Disentanglement

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arxiv 1910.05587 v3 pith:VZLMEQHO submitted 2019-10-12 cs.LG stat.ML

classification cs.LGstat.ML
keywords disentanglementmetricspropertiesrepresentationsaccordingassignconventionaldefinition
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To evaluate disentangled representations several metrics have been proposed. However, theoretical guarantees for conventional metrics of disentanglement are missing. Moreover, conventional metrics do not have a consistent correlation with the outcomes of qualitative studies. In this paper we analyze metrics of disentanglement and their properties. We conclude that existing metrics of disentanglement were created to reflect different characteristics of disentanglement and do not satisfy two basic desirable properties: (1) assign a high score to representations that are disentangled according to the definition; and (2) assign a low score to representations that are entangled according to the definition. In addition, we propose a new metric of disentanglement and prove that it satisfies both of the properties.

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  1. Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A unified representation evaluation protocol shows that models with similar downstream accuracy differ substantially in informativeness, equivariance, invariance, and disentanglement.

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