767 citations · 1.8k across the 46 of their papers we have counts for
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On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset
Muhammad Waleed Gondal, Manuel Wüthrich, Đorđe Miladinović +7
Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notori…
On the Fairness of Disentangled Representations
Francesco Locatello, Gabriele Abbati, Tom Rainforth +3
Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster l…
Are Disentangled Representations Helpful for Abstract Visual Reasoning?
Sjoerd van Steenkiste, Francesco Locatello, Jürgen Schmidhuber +1
A disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is…
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
Luigi Gresele, Paul K. Rubenstein, Arash Mehrjou +2
We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple…
Disentangling Factors of Variation Using Few Labels
Francesco Locatello, Michael Tschannen, Stefan Bauer +3
Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentangleme…
Stochastic Frank-Wolfe for Composite Convex Minimization
Francesco Locatello, Alp Yurtsever, Olivier Fercoq +1
A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to so…