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pro vyhledávání: '"Bellaard, Gijs"'
PDE-based Group Convolutional Neural Networks (PDE-G-CNNs) utilize solvers of geometrically meaningful evolution PDEs as substitutes for the conventional components in G-CNNs. PDE-G-CNNs offer several key benefits all at once: fewer parameters, inher
Externí odkaz:
http://arxiv.org/abs/2403.15182
The roto-translation group SE2 has been of active interest in image analysis due to methods that lift the image data to multi-orientation representations defined on this Lie group. This has led to impactful applications of crossing-preserving flows f
Externí odkaz:
http://arxiv.org/abs/2402.15322
Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. Typically, G-CNNs have the advantage over CNNs that they do not waste network capacity on training symmetries that should have been ha
Externí odkaz:
http://arxiv.org/abs/2210.00935
Publikováno v:
Journal of Mathematical Imaging & Vision; Dec2023, Vol. 65 Issue 6, p819-843, 25p