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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

Categories

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

Keywords

equivariance equivariant convolutions separable convolutions

Licence

MIT

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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.

History

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

Publisher

4TU.ResearchData

Associated peer-reviewed publication

Exploiting Learned Symmetries in Group Equivariant Convolutions

References

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

Funding

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

Organizations

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