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Paper Abstract and Keywords
Presentation 2021-03-29 15:40
A 3DCNN with Reduced Parameters Using Depthwise Separable Convolution
Koki Ito, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) CCS2020-27
Abstract (in Japanese) (See Japanese page) 
(in English) Convolutional Neural Networks (CNNs) have been used in various fields such as image and speech. In recent years, CNNs have been used not only for 2D images but also for 3D video images.
However, these 3-Dimensional CNN (3DCNN) architectures are models that have evolved to compete for the highest accuracy in specific tasks, and the computational complexity and number of parameters have not been discussed so far. This fact has become an obstacle to the application of 3DCNNs.
In this paper, we propose a 3DCNN architecture that can drastically reduce the number of parameters and still maintain the same recognition accuracy among networks that handle 3D information. In our experiments, we have succeeded in reducing the number of parameters by 94.6% in the task of human action recognition.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Convolutional Neural Network / Human Action Recognition / Depthwise Separable Convolution / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 438, CCS2020-27, pp. 37-41, March 2021.
Paper # CCS2020-27 
Date of Issue 2021-03-22 (CCS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
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 CCS2020-27

Conference Information
Committee CCS  
Conference Date 2021-03-29 - 2021-03-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) etc. 
Paper Information
Registration To CCS 
Conference Code 2021-03-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A 3DCNN with Reduced Parameters Using Depthwise Separable Convolution 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Convolutional Neural Network  
Keyword(3) Human Action Recognition  
Keyword(4) Depthwise Separable Convolution  
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1st Author's Name Koki Ito  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Hidehiro Nakano  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
3rd Author's Name Arata Miyauchi  
3rd Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2021-03-29 15:40:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # CCS2020-27 
Volume (vol) vol.120 
Number (no) no.438 
Page pp.37-41 
#Pages 5 
Date of Issue 2021-03-22 (CCS) 

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