_:n8798c06f26af4e279b9770aef7c41418xb35
<http://schema.org/description>
"Although group convolutional networks are able to learn powerful\nrepresentations based on symmetry patterns, they lack explicit means to learn\nmeaningful relationships among them (e.g., relative positions and poses). In\nthis paper, we present attentive group equivariant convolutions, a\ngeneralization of the group convolution, in which attention is applied during\nthe course of convolution to accentuate meaningful symmetry combinations and\nsuppress non-plausible, misleading ones. We indicate that prior work on visual\nattention can be described as special cases of our proposed framework and show\nempirically that our attentive group equivariant convolutional networks\nconsistently outperform conventional group convolutional networks on benchmark\nimage datasets. Simultaneously, we provide interpretability to the learned\nconcepts through the visualization of equivariant attention maps."
<https://deepai.org/profile/mark-hoogendoorn>