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Equivariant Representations for Non-Free Group Actions
Luis Armando Pérez Rey · Giovanni Luca Marchetti · Danica Kragic · Dmitri Jarnikov · Mike Holenderski
Event URL: https://openreview.net/forum?id=2w8j23ZUWC4 »

We introduce a method for learning representations that are equivariant with respect to general group actions over data. Differently from existing equivariant representation learners, our method is suitable for actions that are not free i.e., that stabilize data via nontrivial symmetries. Our method is grounded in the orbit-stabilizer theorem from group theory, which guarantees that an ideal learner infers an isomorphic representation. Finally, we provide an empirical investigation on image datasets with rotational symmetries and show that taking stabilizers into account improves the quality of the representations.

Author Information

Luis Armando Pérez Rey (Eindhoven University of Technology)
Giovanni Luca Marchetti (KTH Royal Institute of Technology, Stockholm, Sweden)
Danica Kragic (KTH Royal Institute of Technology)
Dmitri Jarnikov (Eindhoven University of Technology)
Mike Holenderski (Eindhoven University of Technology)

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