SoftwareApplication
name: Code underlying the publication: Exploiting Learned Symmetries in Group Equivariant Convolutions
description: Code underlying the publication: Exploiting Learned Symmetries in Group Equivariant Convolutions
description: Code corresponding to ICIP 2021 submission "Exploiting Learned Symmetries in Group Equivariant Convolutions". Abstract Group Equivariant Convolutions (GConvs) enable convolutional neural networks to be equivariant to various transformation groups, but at an additional parameter and compute cost. We investigate the filter parameters learned by GConvs and find certain conditions under which they become highly redundant. We show that GConvs can be efficiently decomposed into depthwise separable convolutions while preserving equivariance properties and demonstrate improved performance and data efficiency on two datasets.
code repository: https://data.4tu.nl/v3/datasets/fb66f3b9-5eef-4af6-a0b8-8ed1e63a6f0f.git
author: https://orcid.org/0000-0002-2982-2003
author: https://orcid.org/0000-0002-6913-0482
keywords: equivariance
keywords: separable convolutions
keywords: equivariant convolutions
programming language: Markdown
programming language: Other
programming language: Python
license:
  creative work: creativeWork
    name: MIT
reference publication: icip42928.2021.9506362

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

icip42928.2021.9506362
type: ScholarlyArticle
name: Exploiting Learned Symmetries in Group Equivariant Convolutions
pagination: 759-763
url: https://doi.org/10.1109%2Ficip42928.2021.9506362