Paper Abstract and Keywords |
Presentation |
2021-03-05 13:25
Applying Ensemble Learning in Relay BP Keisuke Toyama, Yukari Yamauchi (Nihon Univ.) NC2020-70 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Convolutional Neural Network (CNN) is one of the network models that can produce highly accurate output even though it uses high dimensional data such as images and audio as input. It has been reported that, in general, the deeper the layers of a CNN, the better the identification accuracy, but simply increasing the depth of the layers does not improve the identification accuracy. This is because, when training a network model, the gradient information degrades as one moves back through the layers from the upper layers to the lower layers. Therefore, Li Shen et al. proposed Relay BP as a new learning method to promote the propagation of useful gradient information. The CNN with Relay BP applied is characterized by the introduction of an intermediate output module in the middle of the network model. The gradient information flowing from the top layer is censored in the middle, and the network model is trained in the lower layers by the gradient information flowing from the intermediate output modules. On the other hand, during estimation, only the output value of the top layer is referred to when determining the output result of the CNN as a whole. Therefore, in this research, the concept of ensemble learning is incorporated into Relay BP to further improve the discrimination accuracy by referring not only to the output values of the top layer, but also to the output values from the intermediate output modules. In the experiment, we compare the results in terms of identification rate and processing time using CIFAR-10, an RGB image dataset, to confirm the effectiveness of the proposed method. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Relay BP / Convolutional Neural Network / Back Propagation / / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 120, no. 403, NC2020-70, pp. 157-162, March 2021. |
Paper # |
NC2020-70 |
Date of Issue |
2021-02-24 (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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NC2020-70 |
Conference Information |
Committee |
NC MBE |
Conference Date |
2021-03-03 - 2021-03-05 |
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(See Japanese page) |
Place (in English) |
Online |
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(See Japanese page) |
Topics (in English) |
Neuro Computing, Medical Engineering, etc. |
Paper Information |
Registration To |
NC |
Conference Code |
2021-03-NC-MBE |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
Applying Ensemble Learning in Relay BP |
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Relay BP |
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Convolutional Neural Network |
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Back Propagation |
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1st Author's Name |
Keisuke Toyama |
1st Author's Affiliation |
Nihon University (Nihon Univ.) |
2nd Author's Name |
Yukari Yamauchi |
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Nihon University (Nihon Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-03-05 13:25:00 |
Presentation Time |
25 minutes |
Registration for |
NC |
Paper # |
NC2020-70 |
Volume (vol) |
vol.120 |
Number (no) |
no.403 |
Page |
pp.157-162 |
#Pages |
6 |
Date of Issue |
2021-02-24 (NC) |
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