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 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) |
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NLP2023-61 |
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) |
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Sentence-BERT |
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representation learning |
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sentence vector |
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Latent Variable |
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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 |
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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 |
4 |
Date of Issue |
2023-11-21 (NLP) |
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