Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation

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.