Breaking the Symmetry: Resolving Symmetry Ambiguities in Equivariant Neural NetworksDownload PDF

26 Sept 2022, 12:09 (modified: 09 Nov 2022, 02:12)NeurReps 2022 PosterReaders: Everyone
Keywords: O(3) equivariance, planar symmetry, 3-D learning, point cloud analysis
TL;DR: We create a rotation equivariant network that is robust to planar symmetric inputs.
Abstract: Equivariant networks have been adopted in many 3-D learning areas. Here we identify a fundamental limitation of these networks: their ambiguity to symmetries. Equivariant networks cannot complete symmetry-dependent tasks like segmenting a left-right symmetric object into its left and right sides. We tackle this problem by adding components that resolve symmetry ambiguities while preserving rotational equivariance. We present OAVNN: Orientation Aware Vector Neuron Network, an extension of the Vector Neuron Network Deng et al. (2021). OAVNN is a rotation equivariant network that is robust to planar symmetric inputs. Our network consists of three key components. 1) We introduce an algorithm to calculate symmetry detecting features. 2) We create a symmetry-sensitive orientation aware linear layer. 3) We construct an attention mechanism that relates directional information across points. We evaluate the network using left-right segmentation and find that the network quickly obtains accurate segmentations. We hope this work motivates investigations on the expressivity of equivariant networks on symmetric objects.
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