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Paper Abstract and Keywords
Presentation 2019-09-05 13:40
A method for visualizing the cause of misrecognition in object recognition using CNN
Tomonori Kubota (Fujitsu Lab.), Yasuyuki Murata (FST), Yoshifumi Uehara, Akira Nakagawa (Fujitsu Lab.) PRMU2019-25 MI2019-44
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a method for visualizing the cause of misrecognition in object recognition using CNN. By this method, it becomes possible to extract and visualize at pixel grain size the place of cause of the image which deteriorates classification probability (Score) of correct answer class in the misrecognition image. And, it is possible to correct the extracted information to the image in which the classification probability of the correct answer class is improved by affecting the misrecognition image, and it can be confirmed that the extracted information correctly shows the cause of the misrecognition. This time, this paper shows the effectiveness of this method by a pre-trained model for discriminating a “car name and model year”.
Keyword (in Japanese) (See Japanese page) 
(in English) object recognition / convolutional neural network / inference / misrecognition / visualizing / XAI / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 193, MI2019-44, pp. 99-104, Sept. 2019.
Paper # MI2019-44 
Date of Issue 2019-08-28 (PRMU, MI) 
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 PRMU2019-25 MI2019-44

Conference Information
Committee PRMU MI IPSJ-CVIM  
Conference Date 2019-09-04 - 2019-09-05 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2019-09-PRMU-MI-CVIM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A method for visualizing the cause of misrecognition in object recognition using CNN 
Sub Title (in English)  
Keyword(1) object recognition  
Keyword(2) convolutional neural network  
Keyword(3) inference  
Keyword(4) misrecognition  
Keyword(5) visualizing  
Keyword(6) XAI  
Keyword(7)  
Keyword(8)  
1st Author's Name Tomonori Kubota  
1st Author's Affiliation Fujitsu Laboratories LTD. (Fujitsu Lab.)
2nd Author's Name Yasuyuki Murata  
2nd Author's Affiliation Fujitsu Software Technologies Limited (FST)
3rd Author's Name Yoshifumi Uehara  
3rd Author's Affiliation Fujitsu Laboratories LTD. (Fujitsu Lab.)
4th Author's Name Akira Nakagawa  
4th Author's Affiliation Fujitsu Laboratories LTD. (Fujitsu Lab.)
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Speaker Author-1 
Date Time 2019-09-05 13:40:00 
Presentation Time 15 minutes 
Registration for MI 
Paper # PRMU2019-25, MI2019-44 
Volume (vol) vol.119 
Number (no) no.192(PRMU), no.193(MI) 
Page pp.99-104 
#Pages 6 
Date of Issue 2019-08-28 (PRMU, MI) 

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