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。

吸引还是分散注意力：开放集空间的边缘利用

Attract or Distract: Exploit the Margin of Open Set

Numerous algorithms have been proposed for transferring knowledge from a
label-rich domain (source) to a label-scarce domain (target). Almost all of
them are proposed for a closed-set scenario, where the source and the target
domain completely share the class of their samples. We call the shared class
the \doublequote{known class.} However, in practice, when samples in target
domain are not labeled, we cannot know whether the domains share the class. A
target domain can contain samples of classes that are not shared by the source
domain. We call such classes the \doublequote{unknown class} and algorithms
that work well in the open set situation are very practical. However, most
existing distribution matching methods for domain adaptation do not work well
in this setting because unknown target samples should not be aligned with the
source.
In this paper, we propose a method for an open set domain adaptation scenario
which utilizes adversarial training. A classifier is trained to make a boundary
between the source and the target samples whereas a generator is trained to
make target samples far from the boundary. Thus, we assign two options to the
feature generator: aligning them with source known samples or rejecting them as
unknown target samples. This approach allows extracting features that separate
unknown target samples from known target samples. Our method was extensively
evaluated in domain adaptation setting and outperformed other methods with a
large margin in most settings.

该研究提出了一种利用对抗训练的开放式域自适应方法，其中分类器被训练用于在源与目标样本之间建立边界，而生成器被训练用于使目标样本远离边界，从而摆脱未知的目标样本，该方法在领域适应设置中得到了广泛的评估，并在大多数设置中优于其他方法。