Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Matrix-in-matrix Neural Network for Image Super Resolution   In recent years, deep learning methods have achieved impressive results with
higher peak signal-to-noise ratio in single image super-resolution (SISR) tasks
by utilizing deeper layers. However, their application is quite limited since
they require high computing power. In addition, most of the existing methods
rarely take full advantage of the intermediate features which are helpful for
restoration. To address these issues, we propose a moderate-size SISR net work
named matrixed channel attention network (MCAN) by constructing a matrix
ensemble of multi-connected channel attention blocks (MCAB). Several models of
different sizes are released to meet various practical requirements.
Conclusions can be drawn from our extensive benchmark experiments that the
proposed models achieve better performance with much fewer multiply-adds and
parameters. Our models will be made publicly available.

15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th