  We address the source-free domain adaptation (SFDA) problem, where only the
source model is available during adaptation to the target domain. We consider
two settings: the offline setting where all target data can be visited multiple
times (epochs) to arrive at a prediction for each target sample, and the online
setting where the target data needs to be directly classified upon arrival.
Inspired by diverse classifier based domain adaptation methods, in this paper
we introduce a second classifier, but with another classifier head fixed. When
adapting to the target domain, the additional classifier initialized from
source classifier is expected to find misclassified features. Next, when
updating the feature extractor, those features will be pushed towards the right
side of the source decision boundary, thus achieving source-free domain
adaptation. Experimental results show that the proposed method achieves
competitive results for offline SFDA on several benchmark datasets compared
with existing DA and SFDA methods, and our method surpasses by a large margin
other SFDA methods under online source-free domain adaptation setting.
