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Paper Abstract and Keywords
Presentation 2025-03-07 11:10
Self-Organizing Incremental Continuous Convolutional Neural Network
Yuta Kobayashi, Haruto Kobayashi, Yukari Yamauchi (Nihon Univ.) NC2024-83
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, continuous convolution has attracted much attention because it can process irregularly sampled data and model long-term dependencies. Sanghyeon Kim et al. proposed self-moving point convolution (SMPConv) with self-moving induced point representation, and achieved improved performance with less computational cost and fewer parameters. However, it is difficult to determine the optimal number of induction points for each experimental data, and the common coordinates and number of induction points for each filter make it difficult to optimize each filter. In order to improve classification accuracy and optimize computational complexity, we propose an algorithm (Self-Organizing Incremental Continuous Convolutional Neural Network: SOICC) to optimize each filter by increasing or decreasing the number of induction points used in the self-moving induction point representation during parameter learning. Experimental comparison with SMPConv on an image classification task using CIFAR10 shows that SOICC can improve the computational speed while maintaining the classification accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Continuous convolution / SMPConv / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 413, NC2024-83, pp. 113-116, March 2025.
Paper # NC2024-83 
Date of Issue 2025-02-26 (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 NC2024-83

Conference Information
Committee NC MBE  
Conference Date 2025-03-05 - 2025-03-07 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Brain architecture, General 
Paper Information
Registration To NC 
Conference Code 2025-03-NC-MBE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Self-Organizing Incremental Continuous Convolutional Neural Network 
Sub Title (in English)  
Keyword(1) Continuous convolution  
Keyword(2) SMPConv  
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1st Author's Name Yuta Kobayashi  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Haruto Kobayashi  
2nd Author's Affiliation Nihon University (Nihon Univ.)
3rd Author's Name Yukari Yamauchi  
3rd Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2025-03-07 11:10:00 
Presentation Time 25 minutes 
Registration for NC 
Paper # NC2024-83 
Volume (vol) vol.124 
Number (no) no.413 
Page pp.113-116 
#Pages 4 
Date of Issue 2025-02-26 (NC) 

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