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Paper Abstract and Keywords
Presentation 2021-03-03 13:00
Hybrid Sparsity in Convolutional Neural Networks
Shoma Noguchi, Yukari Yamauchi (Nihon Univ.) NC2020-46
Abstract (in Japanese) (See Japanese page) 
(in English) Convolutional neural networks (CNNs) have achieved high accuracy in areas such as image classification and object detection. However, large-scale CNN models contain a lot of redundant parameters, which leads to poor generalization performance and overtraining. Alvarez et al. and Scardapane et al. applied sparse group LASSO, which is a combination of group-wise L2 regularization and element-wise L1 regularization, to various architectures. They obtained compressed networks with the same or better accuracy. Kevin et al. proposed sparse-group LASSO L0, in which the L1 regularization term of sparse-group LASSO is replaced by an L0 regularization term. In this study, we propose a hybrid sparsification method that combines sparse-group LASSO and sparse-group LASSO L0, and try to achieve higher sparsity than the conventional method while maintaining the accuracy of CNN.
Keyword (in Japanese) (See Japanese page) 
(in English) Convolutional Neural Network / Sparse Modeling / Sparse Group LASSO / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 403, NC2020-46, pp. 21-24, March 2021.
Paper # NC2020-46 
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)
Download PDF NC2020-46

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) Hybrid Sparsity in Convolutional Neural Networks 
Sub Title (in English)  
Keyword(1) Convolutional Neural Network  
Keyword(2) Sparse Modeling  
Keyword(3) Sparse Group LASSO  
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1st Author's Name Shoma Noguchi  
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 2021-03-03 13:00:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2020-46 
Volume (vol) vol.120 
Number (no) no.403 
Page pp.21-24 
#Pages 4 
Date of Issue 2021-02-24 (NC) 

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