Attentive Group Equivariant Convolutional Networks

7 Feb 2020David W. RomeroErik J. BekkersJakub M. TomczakMark Hoogendoorn

Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (e.g., relative positions and poses). In this paper, we present attentive group equivariant convolutions, a generalization of the group convolution, in which attention is applied during the course of convolution to accentuate meaningful symmetry combinations and suppress non-plausible, misleading ones... (read more)

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