  In Domain Adaptation (DA), where the feature distributions of the source and
target domains are different, various distance-based methods have been proposed
to minimize the discrepancy between the source and target domains to handle the
domain shift. In this paper, we propose a new similarity function, which is
called Population Correlation (PC), to measure the domain discrepancy for DA.
Base on the PC function, we propose a new method called Domain Adaptation by
Maximizing Population Correlation (DAMPC) to learn a domain-invariant feature
representation for DA. Moreover, most existing DA methods use hand-crafted
bottleneck networks, which may limit the capacity and flexibility of the
corresponding model. Therefore, we further propose a method called DAMPC with
Neural Architecture Search (DAMPC-NAS) to search the optimal network
architecture for DAMPC. Experiments on several benchmark datasets, including
Office-31, Office-Home, and VisDA-2017, show that the proposed DAMPC-NAS method
achieves better results than state-of-the-art DA methods.
