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arXiv · 2105.05400

Homogeneous vector bundles and $G$-equivariant convolutional neural networks

Jimmy Aronsson
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Abstract

$G$-equivariant convolutional neural networks (GCNNs) is a geometric deep learning model for data defined on a homogeneous $G$-space $\mathcal{M}$. GCNNs are designed to respect the global symmetry in $\mathcal{M}$, thereby facilitating learning. In this paper, we analyze GCNNs on homogeneous spaces $\mathcal{M} = G/K$ in the case of unimodular Lie groups $G$ and compact subgroups $K \leq G$. We demonstrate that homogeneous vector bundles is the natural setting for GCNNs. We also use reproducing kernel Hilbert spaces to obtain a precise criterion for expressing $G$-equivariant layers as convolutional layers. This criterion is then rephrased as a bandwidth criterion, leading to even stronger results for some groups.

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Jimmy Aronsson. 2021-05-12. Homogeneous vector bundles and $G$-equivariant convolutional neural networks. https://doi.org/10.1007/s43670-022-00029-3

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