Unsupervised Learning of Group Invariant and Equivariant Representations

arXiv:2202.07559v3 Announce Type: replace
Abstract: Equivariant neural networks, whose hidden features transform according to representations of a group G acting on the data, exhibit training efficiency and an improved generalisation performance. In this work, we extend group invariant and equivariant representation learning to the field of unsupervised deep learning. We propose a general learning strategy based on an encoder-decoder framework in which the latent representation is separated in an invariant term and an equivariant group action component. The key idea …

abstract acting arxiv cs.lg data deep learning efficiency features hidden networks neural networks performance representation representation learning training type unsupervised unsupervised learning work