  Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain
by utilizing transferable information learned from the available source domain
and a few labeled target data. However, source data is not always accessible in
practical scenarios, which restricts the application of SSDA in real world
circumstances. In this paper, we propose a novel task named Semi-supervised
Source Hypothesis Transfer (SSHT), which performs domain adaptation based on
source trained model, to generalize well in target domain with a few
supervisions. In SSHT, we are facing two challenges: (1) The insufficient
labeled target data may result in target features near the decision boundary,
with the increased risk of mis-classification; (2) The data are usually
imbalanced in source domain, so the model trained with these data is biased.
The biased model is prone to categorize samples of minority categories into
majority ones, resulting in low prediction diversity. To tackle the above
issues, we propose Consistency and Diversity Learning (CDL), a simple but
effective framework for SSHT by facilitating prediction consistency between two
randomly augmented unlabeled data and maintaining the prediction diversity when
adapting model to target domain. Encouraging consistency regularization brings
difficulty to memorize the few labeled target data and thus enhances the
generalization ability of the learned model. We further integrate Batch
Nuclear-norm Maximization into our method to enhance the discriminability and
diversity. Experimental results show that our method outperforms existing SSDA
methods and unsupervised model adaptation methods on DomainNet, Office-Home and
Office-31 datasets. The code is available at
https://github.com/Wang-xd1899/SSHT.
