The Enforced Transfer: An Instance-Based Divide-and-Conquer Unsupervised Domain Adaptation Algorithm

Existing Domain Adaptation (DA) algorithms train target models to classify all samples in the target domain, but it fails to recognize the possibility that, within the target domain, some samples are closer to the source domain and thus should be classified by source domain models. In this paper, we develop a novel unsupervised DA algorithm, the Enforced Transfer, which employs an out-of-distribution detection algorithm to decide which model (i.e., source domain or target domain) to apply on the testing instance, i.e., divide-and-conquer. Instead of choosing the models at the instance-level, we make the choice of models at the layers of deep models. On three types of DA tasks, we outperform the state-of-the-art algorithms.