  Common domain adaptation techniques assume that the source domain and the
target domain share an identical label space, which is problematic since when
target samples are unlabeled we have no knowledge on whether the two domains
share the same label space. When this is not the case, the existing methods
fail to perform well because the additional unknown classes are also matched
with the source domain during adaptation. In this paper, we tackle the open set
domain adaptation problem under the assumption that the source and the target
label spaces only partially overlap, and the task becomes when the unknown
classes exist, how to detect the target unknown classes and avoid aligning them
with the source domain. We propose to utilize an instance-level reweighting
strategy for domain adaptation where the weights indicate the likelihood of a
sample belonging to known classes and to model the tail of the entropy
distribution with Extreme Value Theory for unknown class detection. Experiments
on conventional domain adaptation datasets show that the proposed method
outperforms the state-of-the-art models.
