The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Ia adalah isu hangat yang mempercepatkan lapisan rangkaian dan mengurangkan parameter rangkaian dalam rangkaian neural konvolusi (CNN). Dalam makalah ini, kami mencadangkan satu kaedah baru, iaitu, penguraian simetri kernel konvolusi (SDK). Ia berpisah secara simetri k×k biji lilitan menjadi (k×1 dan 1×k) atau (1×k and k×1) biji. Kami menjalankan eksperimen perbandingan model rangkaian yang direka oleh SDK pada set data MNIST dan CIFAR-10. Berbanding dengan CNN yang sepadan, kami memperoleh prestasi pengecaman yang baik, dengan kelajuan 1.1×-1.5× dan lebih daripada 30% pengurangan parameter rangkaian. Keputusan eksperimen menunjukkan kaedah kami berguna dan berkesan untuk CNN dalam amalan, dari segi prestasi mempercepatkan dan pengurangan parameter.
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Salinan
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
Salinan
@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},}
Salinan
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 -