Proves regular representation must appear in latent space of finite-group equivariant encoders and enforces it via auxiliary loss to match specialized equivariant models without added parameters.
Learning disentangled representations and group structure of dynamical environments.Advances in Neural Information Processing Systems, 33:19727–19737, 2020.arXiv:2002.06991
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Algebraic Priors for Approximately Equivariant Networks
Proves regular representation must appear in latent space of finite-group equivariant encoders and enforces it via auxiliary loss to match specialized equivariant models without added parameters.