  Semi-supervised domain adaptation (SSDA) is quite a challenging problem
requiring methods to overcome both 1) overfitting towards poorly annotated data
and 2) distribution shift across domains. Unfortunately, a simple combination
of domain adaptation (DA) and semi-supervised learning (SSL) methods often fail
to address such two objects because of training data bias towards labeled
samples. In this paper, we introduce an adaptive structure learning method to
regularize the cooperation of SSL and DA. Inspired by the multi-views learning,
our proposed framework is composed of a shared feature encoder network and two
classifier networks, trained for contradictory purposes. Among them, one of the
classifiers is applied to group target features to improve intra-class density,
enlarging the gap of categorical clusters for robust representation learning.
Meanwhile, the other classifier, serviced as a regularizer, attempts to scatter
the source features to enhance the smoothness of the decision boundary. The
iterations of target clustering and source expansion make the target features
being well-enclosed inside the dilated boundary of the corresponding source
points. For the joint address of cross-domain features alignment and partially
labeled data learning, we apply the maximum mean discrepancy (MMD) distance
minimization and self-training (ST) to project the contradictory structures
into a shared view to make the reliable final decision. The experimental
results over the standard SSDA benchmarks, including DomainNet and Office-home,
demonstrate both the accuracy and robustness of our method over the
state-of-the-art approaches.
