Lars Veefkind

A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs

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 probabili…