Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning SO(3) Equivariant Representations with Spherical CNNs   We address the problem of 3D rotation equivariance in convolutional neural
networks. 3D rotations have been a challenging nuisance in 3D classification
tasks requiring higher capacity and extended data augmentation in order to
tackle it. We model 3D data with multi-valued spherical functions and we
propose a novel spherical convolutional network that implements exact
convolutions on the sphere by realizing them in the spherical harmonic domain.
Resulting filters have local symmetry and are localized by enforcing smooth
spectra. We apply a novel pooling on the spectral domain and our operations are
independent of the underlying spherical resolution throughout the network. We
show that networks with much lower capacity and without requiring data
augmentation can exhibit performance comparable to the state of the art in
standard retrieval and classification benchmarks.

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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci