Mixup provides interpolated training samples and allows the model to obtain smoother decision boundaries for better generalization. The idea can be naturally applied domain adaptation task, where we mix source target domain-mixed adaptation. However, extension of from classification segmentation (i.e., structured output) is nontrivial. This paper systematically studies impact mixup under adapti...

Unsupervised domain adaptation aims to transfer knowledge from a labeled source an unlabeled target domain. Previous methods focus on learning domain-invariant features decrease the discrepancy between feature distributions as well minimizing error and have made remarkable progress. However, recently proposed theory reveals that such strategy is not sufficient for successful adaptation. It show...