Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VarGNet: Variable Group Convolutional Neural Network for Efficient
  Embedded Computing   In this paper, we propose a novel network design mechanism for efficient
embedded computing. Inspired by the limited computing patterns, we propose to
fix the number of channels in a group convolution, instead of the existing
practice that fixing the total group numbers. Our solution based network, named
Variable Group Convolutional Network (VarGNet), can be optimized easier on
hardware side, due to the more unified computing schemes among the layers.
Extensive experiments on various vision tasks, including classification,
detection, pixel-wise parsing and face recognition, have demonstrated the
practical value of our VarGNet.

13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th