Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc. Most domain adaptation methods learn domain-invariant features with data from both domains available. However, such a strategy might be infeasible in practice when source data are unavailable due to data-privacy concerns. To address this issue, we propose a novel adaptation method via hypothesis transfer without accessing source data at adaptation stage. In order to fully use the limited target data, a semi-supervised mutual enhancement method is proposed, in which entropy minimization and augmented label propagation are used iteratively to perform inter-domain and intra-domain alignments. Compared with state-of-the-art methods, the experimental results on three public datasets demonstrate that our method gets up to 19.9% improvements on semi-supervised adaptation tasks.
@article{arxiv.2107.06735,
title = {Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation},
author = {Ning Ma and Jiajun Bu and Lixian Lu and Jun Wen and Zhen Zhang and Sheng Zhou and Xifeng Yan},
journal= {arXiv preprint arXiv:2107.06735},
year = {2021}
}