TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
This addresses privacy concerns in domain adaptation for applications like personal device data, though it is an incremental advancement in method design.
The paper tackles the problem of privacy leakage in few-shot domain adaptation by introducing a new setting called few-shot hypothesis adaptation (FHA), where only a pre-trained source classifier and few labeled target data are used, and proposes TOHAN, which generates an intermediate domain to train a target classifier, achieving significant performance improvements over baselines.
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.