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Representation Learning for Out-of-distribution Generalization in Downstream Tasks
Frederik Träuble · Andrea Dittadi · Manuel Wuthrich · Felix Widmaier · Peter V Gehler · Ole Winther · Francesco Locatello · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer

Learning data representations that are useful for various downstream tasks is a cornerstone of artificial intelligence. While existing methods are typically evaluated on downstream tasks such as classification or generative image quality, we propose to assess representations through their usefulness in downstream control tasks, such as reaching or pushing objects. By training over 10,000 reinforcement learning policies, we extensively evaluate to what extent different representation properties affect out-of-distribution (OOD) generalization. Finally, we demonstrate zero-shot transfer of these policies from simulation to the real world, without any domain randomization or fine-tuning. This paper aims to establish the first systematic characterization of the usefulness of learned representations for real-world OOD downstream tasks.

Author Information

Frederik Träuble (MPI for Intelligent Systems)
Andrea Dittadi (Technical University of Denmark)
Manuel Wuthrich (Max Planck Institute for Intelligent Systems)
Felix Widmaier (, Max Planck Institute for Intelligent Systems)
Peter V Gehler (Amazon)
Ole Winther (University of Copenhagen)
Francesco Locatello (Amazon)
Olivier Bachem (Google Brain)
Bernhard Schölkopf (MPI for Intelligent Systems Tübingen, Germany)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Stefan Bauer (Max Planck Institute for Intelligent Systems)

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