mlsanityData to 7 Oct 2026

Cost-effective Framework for Gradual Domain Adaptation with Multifidelity

arXiv 2202.04359 ↗First posted 9 Feb 2022stat.ML cs.LGcited by 3

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Abstract (arXiv)

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 the target domain. In previous works, it was assumed that the number of samples in the intermediate domains was sufficiently large; hence, self-training was possible without the need for labeled data. If the number of accessible intermediate domains is restricted, the distances between domains become large, and 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 real-world datasets.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 34 of its 74 references that are in the map. First posted on arXiv 9 Feb 2022.

Builds on

34 of 74 references.

References · 34

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22 more
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Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 0; 2024: 1.

3 papers.

  1. 2022Gradual Domain Adaptation via Normalizing Flowscited by 11
  2. 2022Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variancecited by 4
  3. 2024Progressive Conservative Adaptation for Evolving Target Domainscited by 0