  Motivated by the problem relatedness between unsupervised domain adaptation
(UDA) and semi-supervised learning (SSL), many state-of-the-art UDA methods
adopt SSL principles (e.g., the cluster assumption) as their learning
ingredients. However, they tend to overlook the very domain-shift nature of
UDA. In this work, we take a step further to study the proper extensions of SSL
techniques for UDA. Taking the algorithm of label propagation (LP) as an
example, we analyze the challenges of adopting LP to UDA and theoretically
analyze the conditions of affinity graph/matrix construction in order to
achieve better propagation of true labels to unlabeled instances. Our analysis
suggests a new algorithm of Label Propagation with Augmented Anchors (A$^2$LP),
which could potentially improve LP via generation of unlabeled virtual
instances (i.e., the augmented anchors) with high-confidence label predictions.
To make the proposed A$^2$LP useful for UDA, we propose empirical schemes to
generate such virtual instances. The proposed schemes also tackle the
domain-shift challenge of UDA by alternating between pseudo labeling via
A$^2$LP and domain-invariant feature learning. Experiments show that such a
simple SSL extension improves over representative UDA methods of
domain-invariant feature learning, and could empower two state-of-the-art
methods on benchmark UDA datasets. Our results show the value of further
investigation on SSL techniques for UDA problems.
