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Paper Abstract and Keywords
Presentation 2022-10-25 14:15
Relationship between the Defects in Learning Programs and the Model Distortion on the Convolutional Neural Networks
Takumi Tsuchiya, Kozo Okano, Shinpei Ogata (Shinshu Univ.), Shin Nakajima (NII) SS2022-26 DC2022-32
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the quality issue of machine learning software has become an important concern. When considering the quality of machine learning software, the standard evaluation method is to examine the performance of a deep learning model against a test dataset. In this paper, we assume that flaws in training programs, which is the root cause, appear as distortion of weights in trained CNN models, and evaluated the distortion of weights by a new proposed index using distribution of active neurons and NC (Neuron Coverage), where NC indicates the coverage of active neurons in the model. Using these indices, we conducted comparative experiments on probably correct programs and five defective programs in two datasets and five models and found that the indices were different between correct programs and defective programs, indicating that the indices are effective as an indicator of weight distortion.
Keyword (in Japanese) (See Japanese page) 
(in English) CNN / Neuron Coverage / Learning Program / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 225, SS2022-26, pp. 23-28, Oct. 2022.
Paper # SS2022-26 
Date of Issue 2022-10-18 (SS, DC) 
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 SS2022-26 DC2022-32

Conference Information
Committee DC SS  
Conference Date 2022-10-25 - 2022-10-25 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SS 
Conference Code 2022-10-DC-SS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Relationship between the Defects in Learning Programs and the Model Distortion on the Convolutional Neural Networks 
Sub Title (in English)  
Keyword(1) CNN  
Keyword(2) Neuron Coverage  
Keyword(3) Learning Program  
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1st Author's Name Takumi Tsuchiya  
1st Author's Affiliation Shinshu University (Shinshu Univ.)
2nd Author's Name Kozo Okano  
2nd Author's Affiliation Shinshu University (Shinshu Univ.)
3rd Author's Name Shinpei Ogata  
3rd Author's Affiliation Shinshu University (Shinshu Univ.)
4th Author's Name Shin Nakajima  
4th Author's Affiliation National Institute of Informatics (NII)
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Speaker Author-1 
Date Time 2022-10-25 14:15:00 
Presentation Time 25 minutes 
Registration for SS 
Paper # SS2022-26, DC2022-32 
Volume (vol) vol.122 
Number (no) no.225(SS), no.226(DC) 
Page pp.23-28 
#Pages 6 
Date of Issue 2022-10-18 (SS, DC) 

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