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Paper Abstract and Keywords
Presentation 2022-06-27 17:00
Cost-effective Framework for Gradual Domain Adaptation with Multifidelity
Shogo Sagawa (SOKENDAI), Hideitsu Hino (ISM/RIKEN) NC2022-7 IBISML2022-7
Abstract (in Japanese) (See Japanese page) 
(in English) In domain adaptation, when there is a large distance between the source and target domains, the prediction performance will degrade. Gradual domain adaptation is one of the solutions to such an issue, assuming that we have access to intermediate domains, which shift gradually from the source to target domains. In previous works, it was assumed that the number of samples in the intermediate domains is sufficiently large; hence, self-training was possible without the need for labeled data. If access to an intermediate domain is restricted, self-training will fail. Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is. To solve the trade-off between cost and accuracy, we propose a framework that combines multifidelity and active domain adaptation. The effectiveness of the proposed method is evaluated by experiments with both artificial and real-world datasets.
Keyword (in Japanese) (See Japanese page) 
(in English) Gradual domain adaptation / Active learning / Multifidelity learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 90, IBISML2022-7, pp. 61-68, June 2022.
Paper # IBISML2022-7 
Date of Issue 2022-06-20 (NC, IBISML) 
ISSN Online edition: ISSN 2432-6380
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
Conference Date 2022-06-27 - 2022-06-29 
Place (in Japanese) (See Japanese page) 
Place (in English)  
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Paper Information
Registration To IBISML 
Conference Code 2022-06-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Cost-effective Framework for Gradual Domain Adaptation with Multifidelity 
Sub Title (in English)  
Keyword(1) Gradual domain adaptation  
Keyword(2) Active learning  
Keyword(3) Multifidelity learning  
1st Author's Name Shogo Sagawa  
1st Author's Affiliation The Graduate University for Advanced Studies (SOKENDAI)
2nd Author's Name Hideitsu Hino  
2nd Author's Affiliation The Institute of Statistical Mathematics/RIKEN AIP (ISM/RIKEN)
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Speaker Author-1 
Date Time 2022-06-27 17:00:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2022-7, IBISML2022-7 
Volume (vol) vol.122 
Number (no) no.89(NC), no.90(IBISML) 
Page pp.61-68 
Date of Issue 2022-06-20 (NC, IBISML) 

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