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.

翻译：在适应领域,当源和目标领域之间距离很远时,预测性能将下降。渐进性域适应是解决该问题的办法之一,假设我们能够进入中间域,而中间域则逐渐从源向目标域转移。在以往的工程中,假设中间域的样品数量足够大;因此,无需贴标签的数据,就有可能进行自我培训。如果可进入的中间域的数目受到限制,则区域之间的距离将变得很大,自我培训将失败。实际上,中间域的样品成本将有所不同,而且考虑到中间域越接近目标域,从中间域获取样品的成本越高,这是自然的。为了解决成本与准确性之间的权衡,我们提出了一个框架,将多异性和主动域适应结合起来。拟议方法的有效性将通过与现实世界数据集的实验来评估。