DCI-ES: An Extended Disentanglement Framework with Connections 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.
Keywords
Cite
@article{arxiv.2210.00364,
title = {DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability},
author = {Cian Eastwood and Andrei Liviu Nicolicioiu and Julius von Kügelgen and Armin Kekić and Frederik Träuble and Andrea Dittadi and Bernhard Schölkopf},
journal= {arXiv preprint arXiv:2210.00364},
year = {2023}
}
Comments
Accepted to ICLR 2023