Equivariant Representation Learning via Class-Pose Decomposition
- URL: http://arxiv.org/abs/2207.03116v2
- Date: Mon, 11 Jul 2022 06:10:58 GMT
- Title: Equivariant Representation Learning via Class-Pose Decomposition
- Authors: Giovanni Luca Marchetti, Gustaf Tegn\'er, Anastasiia Varava, Danica
Kragic
- Abstract summary: We introduce a general method for learning representations that are equivariant to symmetries of data.
The components semantically correspond to intrinsic data classes and poses respectively.
Results show that our representations capture the geometry of data and outperform other equivariant representation learning frameworks.
- Score: 17.032782230538388
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a general method for learning representations that are
equivariant to symmetries of data. Our central idea is to decompose the latent
space in an invariant factor and the symmetry group itself. The components
semantically correspond to intrinsic data classes and poses respectively. The
learner is self-supervised and infers these semantics based on relative
symmetry information. The approach is motivated by theoretical results from
group theory and guarantees representations that are lossless, interpretable
and disentangled. We provide an empirical investigation via experiments
involving datasets with a variety of symmetries. Results show that our
representations capture the geometry of data and outperform other equivariant
representation learning frameworks.
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