IEICE TRANSACTIONS on Information
Symmetric Decomposition of Convolution Kernels
Summary :
It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.1 pp.219-222
- Publication Date
- 2019/01/01
- Publicized
- 2018/10/18
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8136
- Type of Manuscript
- LETTER
- Category
- Biocybernetics, Neurocomputing
Authors
Keyword
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Jun OU, Yujian LI, "Symmetric Decomposition of Convolution Kernels" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 1, pp. 219-222, January 2019, doi: 10.1587/transinf.2018EDL8136.
Abstract: It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8136/_p
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@ARTICLE{e102-d_1_219,
author={Jun OU, Yujian LI, },
journal={IEICE TRANSACTIONS on Information},
title={Symmetric Decomposition of Convolution Kernels},
year={2019},
volume={E102-D},
number={1},
pages={219-222},
abstract={It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.},
keywords={},
doi={10.1587/transinf.2018EDL8136},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Symmetric Decomposition of Convolution Kernels
T2 - IEICE TRANSACTIONS on Information
SP - 219
EP - 222
AU - Jun OU
AU - Yujian LI
PY - 2019
DO - 10.1587/transinf.2018EDL8136
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2019
AB - It is a hot issue that speeding up the network layers and decreasing the network parameters in convolutional neural networks (CNNs). In this paper, we propose a novel method, namely, symmetric decomposition of convolution kernels (SDKs). It symmetrically separates k×k convolution kernels into (k×1 and 1×k) or (1×k and k×1) kernels. We conduct the comparison experiments of the network models designed by SDKs on MNIST and CIFAR-10 datasets. Compared with the corresponding CNNs, we obtain good recognition performance, with 1.1×-1.5× speedup and more than 30% reduction of network parameters. The experimental results indicate our method is useful and effective for CNNs in practice, in terms of speedup performance and reduction of parameters.
ER -
English
