  Unsupervised domain adaptation (UDA) typically carries out knowledge transfer
from a label-rich source domain to an unlabeled target domain by adversarial
learning. In principle, existing UDA approaches mainly focus on the global
distribution alignment between domains while ignoring the intrinsic local
distribution properties. Motivated by this observation, we propose an
end-to-end structure-conditioned adversarial learning scheme (SCAL) that is
able to preserve the intra-class compactness during domain distribution
alignment. By using local structures as structure-aware conditions, the
proposed scheme is implemented in a structure-conditioned adversarial learning
pipeline. The above learning procedure is iteratively performed by alternating
between local structures establishment and structure-conditioned adversarial
learning. Experimental results demonstrate the effectiveness of the proposed
scheme in UDA scenarios.
