  Unsupervised domain adaptation (UDA) aims to learn the unlabeled target
domain by transferring the knowledge of the labeled source domain. To date,
most of the existing works focus on the scenario of one source domain and one
target domain (1S1T), and just a few works concern the scenario of multiple
source domains and one target domain (mS1T). While, to the best of our
knowledge, almost no work concerns the scenario of one source domain and
multiple target domains (1SmT), in which these unlabeled target domains may not
necessarily share the same categories, therefore, contrasting to mS1T, 1SmT is
more challenging. Accordingly, for such a new UDA scenario, we propose a UDA
framework through the model parameter adaptation (PA-1SmT). A key ingredient of
PA-1SmT is to transfer knowledge through adaptive learning of a common model
parameter dictionary, which is completely different from existing popular
methods for UDA, such as subspace alignment, distribution matching etc., and
can also be directly used for DA of privacy protection due to the fact that the
knowledge is transferred just via the model parameters rather than data itself.
Finally, our experimental results on three domain adaptation benchmark datasets
demonstrate the superiority of our framework.
