  Unsupervised Domain adaptation (UDA) attempts to recognize the unlabeled
target samples by building a learning model from a differently-distributed
labeled source domain. Conventional UDA concentrates on extracting
domain-invariant features through deep adversarial networks. However, most of
them seek to match the different domain feature distributions, without
considering the task-specific decision boundaries across various classes. In
this paper, we propose a novel Adversarial Dual Distinct Classifiers Network
(AD$^2$CN) to align the source and target domain data distribution
simultaneously with matching task-specific category boundaries. To be specific,
a domain-invariant feature generator is exploited to embed the source and
target data into a latent common space with the guidance of discriminative
cross-domain alignment. Moreover, we naturally design two different structure
classifiers to identify the unlabeled target samples over the supervision of
the labeled source domain data. Such dual distinct classifiers with various
architectures can capture diverse knowledge of the target data structure from
different perspectives. Extensive experimental results on several cross-domain
visual benchmarks prove the model's effectiveness by comparing it with other
state-of-the-art UDA.
