The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/5be76022-2db7-4d5d-acb8-6d42fa86f0df

Attila Lengyel; van Gemert, Jan (2023): Code underlying the publication: Exploiting Learned Symmetries in Group Equivariant Convolutions. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/5be76022-2db7-4d5d-acb8-6d42fa86f0df.v1

Software

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

equivariance, equivariant convolutions, separable convolutions

MIT

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Attila Lengyel , Jan van Gemert

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.

• 2023-11-29 first online, published, posted

4TU.ResearchData

Exploiting Learned Symmetries in Group Equivariant Convolutions

• https://research.tudelft.nl/en/publications/exploiting-learned-symmetries-in-group-equivariant-convolutions

• Tabula Inscripta: Prior knowledge for deep learning (grant code VI.Vidi.192.100) [more info...] Dutch Research Council

TU Delft, TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Vision Lab