Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

Understand

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain.

• Recently,adversarial learning with bi-classifier has been proven effective in pushing cross-domain distributions close.
• Prior approaches typically leverage the disagreement between bi-classifier to learn transferable representations, however, they often neglect the classifier determinacy in the target domain, which could result in a lack of feature discriminability.
• In this paper, we present a simple yet effective method, namely Bi-Classifier Determinacy Maximization(BCDM), to tackle this problem.