  We study the implicit bias of gradient flow on linear equivariant steerable
networks in group-invariant binary classification. Our findings reveal that the
parameterized predictor converges in direction to the unique group-invariant
classifier with a maximum margin defined by the input group action. Under a
unitary assumption on the input representation, we establish the equivalence
between steerable networks and data augmentation. Furthermore, we demonstrate
the improved margin and generalization bound of steerable networks over their
non-invariant counterparts.

Machine Learning;Optimization and Control