A unified representation evaluation protocol shows that models with similar downstream accuracy differ substantially in informativeness, equivariance, invariance, and disentanglement.
How to Not Measure Disentanglement
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
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.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Towards a Unified Representation Evaluation Framework Beyond Downstream Tasks
A unified representation evaluation protocol shows that models with similar downstream accuracy differ substantially in informativeness, equivariance, invariance, and disentanglement.