  Unsupervised domain adaptation studies the problem of utilizing a relevant
source domain with abundant labels to build predictive modeling for an
unannotated target domain. Recent work observe that the popular adversarial
approach of learning domain-invariant features is insufficient to achieve
desirable target domain performance and thus introduce additional training
constraints, e.g. cluster assumption. However, these approaches impose the
constraints on source and target domains individually, ignoring the important
interplay between them. In this work, we propose to enforce training
constraints across domains using mixup formulation to directly address the
generalization performance for target data. In order to tackle potentially huge
domain discrepancy, we further propose a feature-level consistency regularizer
to facilitate the inter-domain constraint. When adding intra-domain mixup and
domain adversarial learning, our general framework significantly improves
state-of-the-art performance on several important tasks from both image
classification and human activity recognition.
