Related papers: Evaluating Disentanglement of Structured Latent Representations

Evaluating Disentanglement of Structured Latent Representations

URL: http://arxiv.org/abs/2101.04041v1
Date: Mon, 11 Jan 2021 17:24:01 GMT
Title: Evaluating Disentanglement of Structured Latent Representations
Authors: Rapha\"el Dang-Nhu and Angelika Steger
Abstract summary: We design the first multi-layer disentanglement metric operating at all hierarchy levels of a structured latent representation. Our metric unifies the evaluation of both object separation between latent slots and internal slot disentanglement into a common mathematical framework.
Score: 3.756550107432323
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We design the first multi-layer disentanglement metric operating at all hierarchy levels of a structured latent representation, and derive its theoretical properties. Applied to object-centric representations, our metric unifies the evaluation of both object separation between latent slots and internal slot disentanglement into a common mathematical framework. It also addresses the problematic dependence on segmentation mask sharpness of previous pixel-level segmentation metrics such as ARI. Perhaps surprisingly, our experimental results show that good ARI values do not guarantee a disentangled representation, and that the exclusive focus on this metric has led to counterproductive choices in some previous evaluations. As an additional technical contribution, we present a new algorithm for obtaining feature importances that handles slot permutation invariance in the representation.

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