Authors: Taco S. Cohen,Max Welling
ArXiv: 1612.08498
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Abstract URL: http://arxiv.org/abs/1612.08498v1
It has long been recognized that the invariance and equivariance properties
of a representation are critically important for success in many vision tasks.
In this paper we present Steerable Convolutional Neural Networks, an efficient
and flexible class of equivariant convolutional networks. We show that
steerable CNNs achieve state of the art results on the CIFAR image
classification benchmark. The mathematical theory of steerable representations
reveals a type system in which any steerable representation is a composition of
elementary feature types, each one associated with a particular kind of
symmetry. We show how the parameter cost of a steerable filter bank depends on
the types of the input and output features, and show how to use this knowledge
to construct CNNs that utilize parameters effectively.