| Presentation | 2022-06-27 Cost-effective Framework for Gradual Domain Adaptation with Multifidelity Shogo Sagawa, Hideitsu Hino, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | Gradual domain adaptation / Active learning / Multifidelity learning |
| Paper # | NC2022-7,IBISML2022-7 |
| Date of Issue | 2022-06-20 (NC, IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
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| Conference Date | 2022/6/27(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Hiroshi Yamakawa(Univ of Tokyo) / Masashi Sugiyama(Univ. of Tokyo) |
| Vice Chair | Hirokazu Tanaka(Tokyo City Univ.) / Toshihiro Kamishima(AIST) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Hirokazu Tanaka(NTT) / Toshihiro Kamishima(NICT) / Koji Tsuda(NTT) / (Hokkaido Univ.) |
| Assistant | Yoshimasa Tawatsuji(Waseda Univ.) / Tomoki Kurikawa(KMU) / Yoshinobu Kawahara(Osaka Univ.) / Taiji Suzuki(Tokyo Inst. of Tech.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Bioinformatics and Genomics / Special Interest Group on Mathematical Modeling and Problem Solving |
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| Language | JPN |
| 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) |
| Date | 2022-06-27 |
| Paper # | NC2022-7,IBISML2022-7 |
| Volume (vol) | vol.122 |
| Number (no) | NC-89,IBISML-90 |
| Page | pp.pp.61-68(NC), pp.61-68(IBISML), |
| #Pages | 8 |
| Date of Issue | 2022-06-20 (NC, IBISML) |