  Unsupervised domain adaptation aims to transfer the classifier learned from
the source domain to the target domain in an unsupervised manner. With the help
of target pseudo-labels, aligning class-level distributions and learning the
classifier in the target domain are two widely used objectives. Existing
methods often separately optimize these two individual objectives, which makes
them suffer from the neglect of the other. However, optimizing these two
aspects together is not trivial. To alleviate the above issues, we propose a
novel method that jointly optimizes semantic domain alignment and target
classifier learning in a holistic way. The joint optimization mechanism can not
only eliminate their weaknesses but also complement their strengths. The
theoretical analysis also verifies the favor of the joint optimization
mechanism. Extensive experiments on benchmark datasets show that the proposed
method yields the best performance in comparison with the state-of-the-art
unsupervised domain adaptation methods.

Machine Learning;Machine Learning