RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks

ICLR 2019 Xiuyuan ChengQiang QiuRobert CalderbankGuillermo Sapiro

Explicit encoding of group actions in deep features makes it possible for convolutional neural networks (CNNs) to handle global deformations of images, which is critical to success in many vision tasks. This paper proposes to decompose the convolutional filters over joint steerable bases across the space and the group geometry simultaneously, namely a rotation-equivariant CNN with decomposed convolutional filters (RotDCF)... (read more)

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