  In few-shot domain adaptation (FDA), classifiers for the target domain are
trained with accessible labeled data in the source domain (SD) and few labeled
data in the target domain (TD). However, data usually contain private
information in the current era, e.g., data distributed on personal phones.
Thus, the private information will be leaked if we directly access data in SD
to train a target-domain classifier (required by FDA methods). In this paper,
to thoroughly prevent the privacy leakage in SD, we consider a very challenging
problem setting, where the classifier for the TD has to be trained using few
labeled target data and a well-trained SD classifier, named few-shot hypothesis
adaptation (FHA). In FHA, we cannot access data in SD, as a result, the private
information in SD will be protected well. To this end, we propose a target
orientated hypothesis adaptation network (TOHAN) to solve the FHA problem,
where we generate highly-compatible unlabeled data (i.e., an intermediate
domain) to help train a target-domain classifier. TOHAN maintains two deep
networks simultaneously, where one focuses on learning an intermediate domain
and the other takes care of the intermediate-to-target distributional
adaptation and the target-risk minimization. Experimental results show that
TOHAN outperforms competitive baselines significantly.
