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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. 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