  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. Motivated by the observation that
target samples cannot always be separated distinctly by the decision boundary,
here in the proposed BCDM, we design a novel classifier determinacy disparity
(CDD) metric, which formulates classifier discrepancy as the class relevance of
distinct target predictions and implicitly introduces constraint on the target
feature discriminability. To this end, the BCDM can generate discriminative
representations by encouraging target predictive outputs to be consistent and
determined, meanwhile, preserve the diversity of predictions in an adversarial
manner. Furthermore, the properties of CDD as well as the theoretical
guarantees of BCDM's generalization bound are both elaborated. Extensive
experiments show that BCDM compares favorably against the existing
state-of-the-art domain adaptation methods.
