  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.
