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Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

Abstract : Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents. Higgins et al. (2018) recently proposed Symmetry-Based Disentangled Representation Learning, a definition based on a characterization of symmetries in the environment using group theory. We build on their work and make observations, theoretical and empirical, that lead us to argue that Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries. Our experiments can be reproduced in Colab and the code is available on GitHub.
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Contributor : Hugo Caselles-Dupré <>
Submitted on : Monday, November 25, 2019 - 4:26:33 PM
Last modification on : Thursday, January 21, 2021 - 9:26:01 AM
Long-term archiving on: : Wednesday, February 26, 2020 - 9:06:16 PM


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  • HAL Id : hal-02379399, version 1
  • ARXIV : 1904.00243



Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat. Symmetry-Based Disentangled Representation Learning requires Interaction with Environments. NeurIPS 2019 6 Neural Information Processing Conference, Dec 2019, Vancouver, Canada. ⟨hal-02379399⟩



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