Paper Abstract and Keywords |
Presentation |
2019-12-06 16:55
Evaluation of the visualization techniques providing explanations for decisions of convolutional neural networks Mizuki Mori, Hiroki Tanaka (Kyoto-Sangyo Univ) MBE2019-59 NC2019-50 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recent work has proposed a variety of techniques to visualize what a convolutional neural networks (CNN) utilizes to classify input images into particular classes. However, no established method can objectively evaluate which one provides better visual explanation. In this study, we present a method that directly evaluates the performance of the visualization method based only on changes in the output of the CNN, regardless of whether the visualized features are used by human for image classification. We devised a score measuring how much the output of the CNN's maximum response class decreases when the input is a masked image in which image region corresponding to the visualized features is masked, compared to when the input is an image without masking. By comparing the score when the mask is derived from the visualized features of the maximum response class with the score when the mask is derived from those for the remaining classes, the present method evaluates whether the visualization method produces features specifically relevant to individual classes. We applied the present method to several representative visualization algorithms. Our results suggest that there is a considerable difference in the explanation ability between them. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Convolutional neural network / Visualization / Visual explanation / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 328, NC2019-50, pp. 85-88, Dec. 2019. |
Paper # |
NC2019-50 |
Date of Issue |
2019-11-29 (MBE, NC) |
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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MBE2019-59 NC2019-50 |
Conference Information |
Committee |
NC MBE |
Conference Date |
2019-12-06 - 2019-12-06 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Toyohashi Tech |
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Paper Information |
Registration To |
NC |
Conference Code |
2019-12-NC-MBE |
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Japanese |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
Evaluation of the visualization techniques providing explanations for decisions of convolutional neural networks |
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Convolutional neural network |
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Visualization |
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Visual explanation |
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1st Author's Name |
Mizuki Mori |
1st Author's Affiliation |
Kyoto-Sangyo University (Kyoto-Sangyo Univ) |
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Hiroki Tanaka |
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Kyoto-Sangyo University (Kyoto-Sangyo Univ) |
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Speaker |
Author-1 |
Date Time |
2019-12-06 16:55:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
MBE2019-59, NC2019-50 |
Volume (vol) |
vol.119 |
Number (no) |
no.327(MBE), no.328(NC) |
Page |
pp.85-88 |
#Pages |
4 |
Date of Issue |
2019-11-29 (MBE, NC) |
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