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) |
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CCS2020-27 |
Conference Information |
Committee |
CCS |
Conference Date |
2021-03-29 - 2021-03-29 |
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(See Japanese page) |
Place (in English) |
Online |
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(See Japanese page) |
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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) |
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Keyword(1) |
Deep Learning |
Keyword(2) |
Convolutional Neural Network |
Keyword(3) |
Human Action Recognition |
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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 |
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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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