  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
cannot be transferred to the target domain. We propose Domain Adaptation
without Source Model, which refines information from source model. In order to
gain more informative results, we further propose Distributionally Adversarial
Training (DAT) to align the distribution of source data with that of target
data. Experimental results on benchmarks of Digit-Five, Office-Caltech,
Office-31, Office-Home, and DomainNet demonstrate the feasibility of our method
without model transferring.
