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Paper Abstract and Keywords
Presentation 2023-11-28 10:50
Investigation of differences in latent variable space for different datasets in Sentence-BERT's image generation model
Masato Izumi, Kenya Jin'no (Tokyo City Univ.) NLP2023-61
Abstract (in Japanese) (See Japanese page) 
(in English) We have verified the degree to which sentence vectors, which are distributed representations of sentences generated by Sentence-BERT, capture the meaning of sentences using k-means and UMAP, and have confirmed that the sentence vectors generated by Sentence-BERT capture the meaning of sentences extremely well.
We constructed a model for image generation using latent variables output by Sentence-BERT.
By generating images from sentences, we have visualized the latent variables output from the natural language processing model.
In this study, we investigated how the visualization of the latent variable space changes when the dataset of the image generation model is varied.
Keyword (in Japanese) (See Japanese page) 
(in English) Sentence-BERT / representation learning / sentence vector / Latent Variable / Image generation / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 287, NLP2023-61, pp. 11-14, Nov. 2023.
Paper # NLP2023-61 
Date of Issue 2023-11-21 (NLP) 
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 NLP  
Conference Date 2023-11-28 - 2023-11-29 
Place (in Japanese) (See Japanese page) 
Place (in English) Nago city commerce and industry association 
Topics (in Japanese) (See Japanese page) 
Topics (in English) NLP, etc. 
Paper Information
Registration To NLP 
Conference Code 2023-11-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation of differences in latent variable space for different datasets in Sentence-BERT's image generation model 
Sub Title (in English)  
Keyword(1) Sentence-BERT  
Keyword(2) representation learning  
Keyword(3) sentence vector  
Keyword(4) Latent Variable  
Keyword(5) Image generation  
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1st Author's Name Masato Izumi  
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 2023-11-28 10:50:00 
Presentation Time 25 minutes 
Registration for NLP 
Paper # NLP2023-61 
Volume (vol) vol.123 
Number (no) no.287 
Page pp.11-14 
#Pages
Date of Issue 2023-11-21 (NLP) 


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