  Present domain adaptation methods usually perform explicit representation
alignment by simultaneously accessing the source data and target data. However,
the source data are not always available due to the privacy preserving
consideration or bandwidth limitation. Source-free domain adaptation aims to
solve the above problem by performing domain adaptation without accessing the
source data. The adaptation paradigm is receiving more and more attention in
recent years, and multiple works have been proposed for unsupervised
source-free domain adaptation. However, without utilizing any supervised signal
and source data at the adaptation stage, the optimization of the target model
is unstable and fragile. To alleviate the problem, we focus on semi-supervised
domain adaptation under source-free setting. More specifically, we propose
uncertainty-guided Mixup to reduce the representation's intra-domain
discrepancy and perform inter-domain alignment without directly accessing the
source data. Finally, we conduct extensive semi-supervised domain adaptation
experiments on various datasets. Our method outperforms the recent
semi-supervised baselines and the unsupervised variant also achieves
competitive performance. The experiment codes will be released in the future.
