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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 |
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| 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 | |
| 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 | NC2022-7 IBISML2022-7 |
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| Conference Information | |
| Committee | NC IBISML IPSJ-BIO IPSJ-MPS |
| Conference Date | 2022-06-27 - 2022-06-29 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| 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 |
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| 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 |
| #Pages | 8 |
| Date of Issue | 2022-06-20 (NC, IBISML) |
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