Cost-effective Framework for Gradual Domain Adaptation with Multifidelity. (arXiv:2202.04359v3 [stat.ML] UPDATED)

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 …

arxiv cost domain adaptation framework