Tools
Tools
Durrant, Aiden 
ORCID: https://orcid.org/0000-0002-8375-4523, Baburajan, Rahul and Leontidis, Georgios
(2026)
Group-Equivariant Poincaré Convolutional Networks.
In: European Conference on Computer Vision, 2014-11-01.
(In Press)
Abstract
While recent methods like that of the Poincaré ResNet have demonstrated the ability to learning visual representations directly in hyperbolic space, their optimisation remains a challenge, primarily due to the parameter redundancy of learning distinct orientation filters. In addition, hyperbolic learning exhibits distinct computational overheads that limit their wide use, where efforts to improve their efficiency via optimisation have seen good success, there has been limited exploration into structural priors that enable stronger sample efficiency at training. To address this, we propose Equivariant Poincaré ResNets, combining hyperbolic geometry with discrete symmetry groups (C4 and D4). We identify critical roadblocks in applying Euclidean equivariance to hyperbolic space and propose geometrically safe tensor reshaping, left-regular permutations for hyperbolic group convolutions, and joint-orientation Poincaré Midpoint Batch normalisation. Empirical evaluations show that embedding equivariance significantly improves the sample efficiency during training which in-turn accelerates convergence while respecting the boundary constraints of the Poincaré ball and retaining spatial group equivariance.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | 3* ,/dk/atira/pure/researchoutput/REFrank/3_ |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Sep 2026 16:15 |
| Last Modified: | 17 Sep 2026 16:15 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/104583 |
| DOI: |
Actions (login required)
 |
View Item |