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Paper Abstract and Keywords
Presentation 2024-05-10 10:55
Analysis of the role of latent variables in image classification in deep learning models
Kenya Jin'no, Mizuki Dai, Haruki Wakasa, Hiroki Tamegai (Tokyo City Univ.) NLP2024-12
Abstract (in Japanese) (See Japanese page) 
(in English) Deep learning-based image classification represents a significant advancement in enabling computers to identify the content of images. This process involves a component known as the feature extractor, which extracts vital features from images, followed by a classifier that uses this information to determine the category to which an image belongs. For enhancing classification accuracy, it is crucial to efficiently extract latent features, which are the hidden information within images.

Despite the high accuracy of some models, the detailed mechanisms through which these latent features are effectively extracted remain insufficiently understood. In this study, we delve into how the feature extractor contributes to information retrieval from images. Specifically, we analyze the nature of feature vectors generated when employing categorical cross-entropy and how these vectors aid the classifier's decision-making process.

By representing these feature vectors in two dimensions, we can visually depict them, deepening our understanding of the interplay between feature extractors and classifiers. Through this approach, our goal is to elucidate the underlying mechanisms of high-precision image classification models, bringing us closer to unraveling the complexities behind efficient feature extraction and classification.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep Learning / Image Classifier / Latent Space / Categorical Cross Entropy / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 13, NLP2024-12, pp. 58-62, May 2024.
Paper # NLP2024-12 
Date of Issue 2024-05-02 (NLP) 
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 NLP2024-12

Conference Information
Committee NLP  
Conference Date 2024-05-09 - 2024-05-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Kagawa Prefecture Social Welfare Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear Problems, etc. 
Paper Information
Registration To NLP 
Conference Code 2024-05-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Analysis of the role of latent variables in image classification in deep learning models 
Sub Title (in English)  
Keyword(1) Deep Learning  
Keyword(2) Image Classifier  
Keyword(3) Latent Space  
Keyword(4) Categorical Cross Entropy  
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Keyword(6)  
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1st Author's Name Kenya Jin'no  
1st Author's Affiliation Tokyo City University (Tokyo City Univ.)
2nd Author's Name Mizuki Dai  
2nd Author's Affiliation Tokyo City University (Tokyo City Univ.)
3rd Author's Name Haruki Wakasa  
3rd Author's Affiliation Tokyo City University (Tokyo City Univ.)
4th Author's Name Hiroki Tamegai  
4th Author's Affiliation Tokyo City University (Tokyo City Univ.)
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Speaker Author-1 
Date Time 2024-05-10 10:55:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2024-12 
Volume (vol) vol.124 
Number (no) no.13 
Page pp.58-62 
#Pages 5 
Date of Issue 2024-05-02 (NLP) 

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