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Paper Abstract and Keywords
Presentation 2018-03-08 14:20
A Study of Kernel Clustering for Reducing Memory Footprint of CNN
Yuki Matsui, Shinobu Miwa (UEC), Satoshi Shindo, Tomoaki Tsumura (NITech), Hayato Yamaki, Hiroki Honda (UEC) CPSY2017-140 DC2017-96
Abstract (in Japanese) (See Japanese page) 
(in English) Convolutional Neural Network (CNN) is widely used in the field of image recognition due to the high recognition accuracy. CNN is a sort of deep and large-scale neural networks so that it has numbers of parameters to be used for the computation. There have been many studies of compressing the data of CNN such as reducing the numbers of parameters and bits of parameters. Meanwhile, a well-trained CNN has very regular structure (i.e., 2D kernels) available for data compression, but no study of exploiting this structure for data compression in CNN has been reported so far. We have proposed a technique that clusters 2D kernels trained and replaces them with representative 2D kernels for reducing the number of parameters in CNN. In this paper, we report the experimental results of clustering the overall 2D kernels within VGG-16 with various numbers of clusters. Our experimental results show that the proposed technique can reduce the number of kernels by 85.6% in exchange for a 9% reduction in the recognition accuracy. The proposed technique is orthogonal to the other approaches of compressing the data in CNN, such as pruning and quantization; hence, they can be used together to obtain further gains.
Keyword (in Japanese) (See Japanese page) 
(in English) CNN / data compression / clustering / / / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 479, CPSY2017-140, pp. 185-190, March 2018.
Paper # CPSY2017-140 
Date of Issue 2018-02-28 (CPSY, DC) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF CPSY2017-140 DC2017-96

Conference Information
Committee CPSY DC IPSJ-SLDM IPSJ-EMB IPSJ-ARC  
Conference Date 2018-03-07 - 2018-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinoshima Bunka-Kaikan Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) ETNET2018 
Paper Information
Registration To CPSY 
Conference Code 2018-03-CPSY-DC-SLDM-EMB-ARC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Kernel Clustering for Reducing Memory Footprint of CNN 
Sub Title (in English)  
Keyword(1) CNN  
Keyword(2) data compression  
Keyword(3) clustering  
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1st Author's Name Yuki Matsui  
1st Author's Affiliation The University of Electro-Communications (UEC)
2nd Author's Name Shinobu Miwa  
2nd Author's Affiliation The University of Electro-Communications (UEC)
3rd Author's Name Satoshi Shindo  
3rd Author's Affiliation Nagoya Institute of Technology (NITech)
4th Author's Name Tomoaki Tsumura  
4th Author's Affiliation Nagoya Institute of Technology (NITech)
5th Author's Name Hayato Yamaki  
5th Author's Affiliation The University of Electro-Communications (UEC)
6th Author's Name Hiroki Honda  
6th Author's Affiliation The University of Electro-Communications (UEC)
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Speaker Author-1 
Date Time 2018-03-08 14:20:00 
Presentation Time 25 minutes 
Registration for CPSY 
Paper # CPSY2017-140, DC2017-96 
Volume (vol) vol.117 
Number (no) no.479(CPSY), no.480(DC) 
Page pp.185-190 
#Pages
Date of Issue 2018-02-28 (CPSY, DC) 


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