  Open-Set Domain Adaptation (OSDA) assumes that a target domain contains
unknown classes, which are not discovered in a source domain. Existing domain
adversarial learning methods are not suitable for OSDA because distribution
matching with $\textit{unknown}$ classes leads to negative transfer. Previous
OSDA methods have focused on matching the source and the target distribution by
only utilizing $\textit{known}$ classes. However, this $\textit{known}$-only
matching may fail to learn the target-$\textit{unknown}$ feature space.
Therefore, we propose Unknown-Aware Domain Adversarial Learning (UADAL), which
$\textit{aligns}$ the source and the target-$\textit{known}$ distribution while
simultaneously $\textit{segregating}$ the target-$\textit{unknown}$
distribution in the feature alignment procedure. We provide theoretical
analyses on the optimized state of the proposed $\textit{unknown-aware}$
feature alignment, so we can guarantee both $\textit{alignment}$ and
$\textit{segregation}$ theoretically. Empirically, we evaluate UADAL on the
benchmark datasets, which shows that UADAL outperforms other methods with
better feature alignments by reporting state-of-the-art performances.
