On Making $\mathrm{SE}(2)$-Invariant Networks Optimal
A plug-and-play module for convolutional neural networks that enforces $\mathrm{SE}(2)$ invariance, capturing both translations and rotations within standard architectures, and derive invariant feature maps that are provably optimal: minimal and information-preserving with respect to the symmetry group.
Tomáš Karella, Emily Shinkle, Alice E. A. Allen et al.
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