767 citations · 2.5k across the 48 of their papers we have counts for
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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The idea behind the \emph{unsupervised} learning of \emph{disentangled} representations is that real-world data is generated by a few explanatory factors of variation which can be…
A Commentary on the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic +4
The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In…
What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study
Marcin Andrychowicz, Anton Raichuk, Piotr Stańczyk +9
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks. While RL algorithms are often conceptually simple,…
Automatic Shortcut Removal for Self-Supervised Representation Learning
Matthias Minderer, Olivier Bachem, Neil Houlsby +1
In self-supervised visual representation learning, a feature extractor is trained on a "pretext task" for which labels can be generated cheaply, without human annotation. A central…
Weakly-Supervised Disentanglement Without Compromises
Francesco Locatello, Ben Poole, Gunnar Rätsch +3
Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at lea…