  Recently, considerable effort has been devoted to deep domain adaptation in
computer vision and machine learning communities. However, most of existing
work only concentrates on learning shared feature representation by minimizing
the distribution discrepancy across different domains. Due to the fact that all
the domain alignment approaches can only reduce, but not remove the domain
shift. Target domain samples distributed near the edge of the clusters, or far
from their corresponding class centers are easily to be misclassified by the
hyperplane learned from the source domain. To alleviate this issue, we propose
to joint domain alignment and discriminative feature learning, which could
benefit both domain alignment and final classification. Specifically, an
instance-based discriminative feature learning method and a center-based
discriminative feature learning method are proposed, both of which guarantee
the domain invariant features with better intra-class compactness and
inter-class separability. Extensive experiments show that learning the
discriminative features in the shared feature space can significantly boost the
performance of deep domain adaptation methods.
