SoftwareApplication
name: Code underlying the publication: Using and Abusing Equivariance
description: Code underlying the publication: Using and Abusing Equivariance
description: Code corresponding to ICCVw 2023 conference workshop paper "Using and Abusing Equivariance". Abstract In this paper we show how Group Equivariant Convolutional Neural Networks use subsampling to learn to break equivariance to their symmetries. We focus on 2D rotations and reflections and investigate the impact of broken equivariance on network performance. We show that a change in the input dimension of a network as small as a single pixel can be enough for commonly used architectures to become approximately equivariant, rather than exactly. We investigate the impact of networks not being exactly equivariant and find that approximately equivariant networks generalise significantly worse to unseen symmetries compared to their exactly equivariant counterparts. However, when the symmetries in the training data are not identical to the symmetries of the network, we find that approximately equivariant networks are able to relax their own equivariant constraints, causing them to match or outperform exactly equivariant networks on common benchmark datasets.
code repository: https://data.4tu.nl/v3/datasets/9fd566e4-f4a8-4585-b705-be66fc29af9b.git
author: https://orcid.org/0000-0002-2982-2003
author: Person
  given name: Tom
  family name: Edixhoven
  name: Tom Edixhoven
author: https://orcid.org/0000-0002-6913-0482
keywords: equivariance
keywords: data efficient deep learning
keywords: computer vision
keywords: equivariant convolutions
programming language: Markdown
programming language: Other
programming language: Python
license:
  creative work: creativeWork
    name: Apache-2.0

https://orcid.org/0000-0002-2982-2003
type: Person
given name: Attila
family name: Lengyel
name: Attila Lengyel

https://orcid.org/0000-0002-6913-0482
type: Person
given name: Jan
family name: van Gemert
name: Jan van Gemert