DCI-ES: An Extended Disentanglement Framework with Connections to IdentifiabilityDownload PDF

Published: 01 Feb 2023, Last Modified: 16 Feb 2023ICLR 2023 posterReaders: Everyone
Keywords: disentanglement, identifiability, representation learning
TL;DR: We extend the DCI framework for evaluating disentangled representations and connect it to identifiability.
Abstract: In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativeness (I). In this work, we first connect this DCI framework to two common notions of linear and nonlinear identifiability, thereby establishing a formal link between disentanglement and the closely-related field of independent component analysis. We then propose an extended DCI-ES framework with two new measures of representation quality—explicitness (E) and size (S)—and point out how D and C can be computed for black-box predictors. Our main idea is that the functional capacity required to use a representation is an important but thus-far neglected aspect of representation quality, which we quantify using explicitness or ease-of-use (E). We illustrate the relevance of our extensions on the MPI3D and Cars3D datasets.
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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
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