June 7, 2024, 4:42 a.m. | Lars Veefkind, Gabriele Cesa

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03946v1 Announce Type: new
Abstract: Steerable convolutional neural networks (SCNNs) enhance task performance by modelling geometric symmetries through equivariance constraints on weights. Yet, unknown or varying symmetries can lead to overconstrained weights and decreased performance. To address this, this paper introduces a probabilistic method to learn the degree of equivariance in SCNNs. We parameterise the degree of equivariance as a likelihood distribution over the transformation group using Fourier coefficients, offering the option to model layer-wise and shared equivariance. These likelihood …

abstract arxiv cnns constraints convolutional convolutional neural networks cs.lg learn modelling networks neural networks paper performance through type

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