  In this work, we connect two distinct concepts for unsupervised domain
adaptation: feature distribution alignment between domains by utilizing the
task-specific decision boundary and the Wasserstein metric. Our proposed sliced
Wasserstein discrepancy (SWD) is designed to capture the natural notion of
dissimilarity between the outputs of task-specific classifiers. It provides a
geometrically meaningful guidance to detect target samples that are far from
the support of the source and enables efficient distribution alignment in an
end-to-end trainable fashion. In the experiments, we validate the effectiveness
and genericness of our method on digit and sign recognition, image
classification, semantic segmentation, and object detection.
