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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
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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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Conference Information
Committee NC MBE  
Conference Date 2021-03-03 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (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) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Applying Ensemble Learning in Relay BP 
Sub Title (in English)  
Keyword(1) Relay BP  
Keyword(2) Convolutional Neural Network  
Keyword(3) 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  
2nd Author's Affiliation 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
Date of Issue 2021-02-24 (NC) 


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