  Unsupervised domain adaptation (UDA) aims to transfer knowledge from a
related but different well-labeled source domain to a new unlabeled target
domain. Most existing UDA methods require access to the source data, and thus
are not applicable when the data are confidential and not shareable due to
privacy concerns. This paper aims to tackle a realistic setting with only a
classification model available trained over, instead of accessing to, the
source data. To effectively utilize the source model for adaptation, we propose
a novel approach called Source HypOthesis Transfer (SHOT), which learns the
feature extraction module for the target domain by fitting the target data
features to the frozen source classification module (representing
classification hypothesis). Specifically, SHOT exploits both information
maximization and self-supervised learning for the feature extraction module
learning to ensure the target features are implicitly aligned with the features
of unseen source data via the same hypothesis. Furthermore, we propose a new
labeling transfer strategy, which separates the target data into two splits
based on the confidence of predictions (labeling information), and then employ
semi-supervised learning to improve the accuracy of less-confident predictions
in the target domain. We denote labeling transfer as SHOT++ if the predictions
are obtained by SHOT. Extensive experiments on both digit classification and
object recognition tasks show that SHOT and SHOT++ achieve results surpassing
or comparable to the state-of-the-arts, demonstrating the effectiveness of our
approaches for various visual domain adaptation problems.
