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• W3037148157 abstract "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." @default.
• W3037148157 created "2020-07-02" @default.
• W3037148157 creator A5083922034 @default.
• W3037148157 creator A5087368991 @default.
• W3037148157 date "2016-12-27" @default.
• W3037148157 modified "2023-09-28" @default.
• W3037148157 title "Steerable CNNs" @default.
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• W3037148157 hasPublicationYear "2016" @default.
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