Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Caselles-Dupré, H., Garcia Ortiz, M. 
ORCID: 0000-0003-4729-7457 & Filliat, D. (2019).
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments.
In:
Advances in Neural Information Processing Systems.
Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), 8-14 Dec 2019, Vancouver, Canada.
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.
| Publication Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Departments: | School of Science & Technology > Computer Science |
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