Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Depth-wise Decomposition for Accelerating Separable Convolutions in
  Efficient Convolutional Neural Networks   Very deep convolutional neural networks (CNNs) have been firmly established
as the primary methods for many computer vision tasks. However, most
state-of-the-art CNNs are large, which results in high inference latency.
Recently, depth-wise separable convolution has been proposed for image
recognition tasks on computationally limited platforms such as robotics and
self-driving cars. Though it is much faster than its counterpart, regular
convolution, accuracy is sacrificed. In this paper, we propose a novel
decomposition approach based on SVD, namely depth-wise decomposition, for
expanding regular convolutions into depthwise separable convolutions while
maintaining high accuracy. We show our approach can be further generalized to
the multi-channel and multi-layer cases, based on Generalized Singular Value
Decomposition (GSVD) [59]. We conduct thorough experiments with the latest
ShuffleNet V2 model [47] on both random synthesized dataset and a large-scale
image recognition dataset: ImageNet [10]. Our approach outperforms channel
decomposition [73] on all datasets. More importantly, our approach improves the
Top-1 accuracy of ShuffleNet V2 by ~2%.

30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph