Black-box Probe for Unsupervised Domain Adaptation without Model Transferring.

In recent years, researchers have been paying increasing attention to the threats brought by deep learning models to data security and privacy, especially in the field of domain adaptation. Existing unsupervised domain adaptation (UDA) methods can achieve promising performance without transferring data from source domain to target domain. However, UDA with representation alignment or self-supervised pseudo-labeling relies on the transferred source models. In many data-critical scenarios, methods based on model transferring may suffer from membership inference attacks and expose private data. In this paper, we aim to overcome a challenging new setting where the source models are only queryable but cannot be transferred to the target domain. We propose Black-box Probe Domain Adaptation (BPDA), which adopts query mechanism to probe and refine information from source model using third-party dataset. In order to gain more informative query results, we further propose Distributionally Adversarial Training (DAT) to align the distribution of third-party data with that of target data. BPDA uses public third-party dataset and adversarial examples based on DAT as the information carrier between source and target domains, dispensing with transferring source data or model. Experimental results on benchmarks of Digit-Five, Office-Caltech, Office-31, Office-Home, and DomainNet demonstrate the feasibility of BPDA without model transferring.