Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Neural Networks for Spherical Signal Processing via
  Spherical Haar Tight Framelets   In this paper, we develop a general theoretical framework for constructing
Haar-type tight framelets on any compact set with a hierarchical partition. In
particular, we construct a novel area-regular hierarchical partition on the
2-sphere and establish its corresponding spherical Haar tight framelets with
directionality. We conclude by evaluating and illustrating the effectiveness of
our area-regular spherical Haar tight framelets in several denoising
experiments. Furthermore, we propose a convolutional neural network (CNN) model
for spherical signal denoising which employs the fast framelet decomposition
and reconstruction algorithms. Experiment results show that our proposed CNN
model outperforms threshold methods, and processes strong generalization and
robustness properties.

20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc