IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2019-10-18 10:45
[Short Paper] An attribution-based pruning method for single object detection network
Rui Shi, Tianxing Li, Yasushi Yamaguchi (UTokyo) PRMU2019-34
Abstract (in Japanese) (See Japanese page) 
(in English) Deep neural networks (DNNs) have achieved advanced results on different vision tasks. However, the cost of high computational complexity is not practical for real-time inferences running on mobile devices. In this paper, we propose an attribution-based pruning method for object detection network that reduce the computation while ensuring accuracy. First, Channel mask and spatial mask are designed to generalize attribution methods to detection networks for detecting convolution kernels that are firmly correlated with target output. Then, YOLOv3-tiny network is pruned using attribution maps and finetuned on an open-sourced mango dataset for evaluation. Compared to a state-of-the-art network trained with the same mango dataset, the experiment shows that our network achieves 83.4% computation reduction with only about 2.4% loss in accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep learning / Mango detection / Network pruning / Attribution methods / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 235, PRMU2019-34, pp. 17-20, Oct. 2019.
Paper # PRMU2019-34 
Date of Issue 2019-10-11 (PRMU) 
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 PRMU2019-34

Conference Information
Committee PRMU  
Conference Date 2019-10-18 - 2019-10-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To PRMU 
Conference Code 2019-10-PRMU 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) An attribution-based pruning method for single object detection network 
Sub Title (in English)  
Keyword(1) Deep learning  
Keyword(2) Mango detection  
Keyword(3) Network pruning  
Keyword(4) Attribution methods  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Rui Shi  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Tianxing Li  
2nd Author's Affiliation The University of Tokyo (UTokyo)
3rd Author's Name Yasushi Yamaguchi  
3rd Author's Affiliation The University of Tokyo (UTokyo)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2019-10-18 10:45:00 
Presentation Time 10 minutes 
Registration for PRMU 
Paper # PRMU2019-34 
Volume (vol) vol.119 
Number (no) no.235 
Page pp.17-20 
#Pages
Date of Issue 2019-10-11 (PRMU) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan