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Paper Abstract and Keywords
Presentation 2024-07-30 11:10
The impact of each layer of feature extractors on class separability
Chisato Takahashi, Kenya Jin'no (Tokyo City Univ.) CCS2024-37
Abstract (in Japanese) (See Japanese page) 
(in English) There is a method called Network Architecture Search (NAS) that automatically explores the optimal structure of neural networks.
As NAS requires an enormous amount of search time, a low-cost performance evaluation method is needed.
We conduct an investigation into the characteristics of classification models for metric creation.
A classification model consists of a feature extractor that extracts features from input data and a classifier that performs class classification based on the extracted features.
The ability of the classifier to perform class classification heavily depends on the feature extractor, so our focus is on the feature extractor.
To establish a foundation for creating evaluation metrics, we focused on the feature extractor within classification models, which extracts features from input and separates them by class.
In this paper, we investigate how each layer of the feature extractor structure affects class separation.
Keyword (in Japanese) (See Japanese page) 
(in English) Classification Model / feature extractor / linear discriminant analysis / Network Architecture Search / one-shot evaluation index / CNN / CIFAR-10 /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 138, CCS2024-37, pp. 48-51, July 2024.
Paper # CCS2024-37 
Date of Issue 2024-07-22 (CCS) 
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 CCS2024-37

Conference Information
Committee IN CCS  
Conference Date 2024-07-29 - 2024-07-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Moerenuma Park Glass Pyramid 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others 
Paper Information
Registration To CCS 
Conference Code 2024-07-IN-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) The impact of each layer of feature extractors on class separability 
Sub Title (in English)  
Keyword(1) Classification Model  
Keyword(2) feature extractor  
Keyword(3) linear discriminant analysis  
Keyword(4) Network Architecture Search  
Keyword(5) one-shot evaluation index  
Keyword(6) CNN  
Keyword(7) CIFAR-10  
Keyword(8)  
1st Author's Name Chisato Takahashi  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Kenya Jin'no  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2024-07-30 11:10:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # CCS2024-37 
Volume (vol) vol.124 
Number (no) no.138 
Page pp.48-51 
#Pages 4 
Date of Issue 2024-07-22 (CCS) 

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