Open set domain adaptation aims to diminish the domain shift across domains,
with partially shared classes. There exist unknown target samples out of the
knowledge of source domain. Compared to the close set setting, how to separate
the unknown (unshared) class from the known (shared) ones plays a key role.
Whereas, previous methods did not emphasize the semantic structure of the open
set data, which may introduce bias into the domain alignment and confuse the
classifier around the decision boundary. In this paper, we exploit the semantic
structure of open set data from two aspects: 1) Semantic Categorical Alignment,
which aims to achieve good separability of target known classes by
categorically aligning the centroid of target with the source. 2)Semantic
Contrastive Mapping, which aims to push the unknown class away from the
decision boundary. Empirically, we demonstrate that our method performs
favourably against the state-of-the-art methods on representative benchmarks,
e.g. Digit datasets and Office-31 datasets.

本文中，我们探讨了如何通过语义分类对齐和对未知类别的映射，提高开放式域适应性的性能。实验证明，我们的方法在现有基准测试上表现优异。