Abstract. Numerous algorithms have been proposed for transferring
knowledge from a label-rich domain (source) to a label-scarce domain
(target). Most of them are proposed for closed-set scenario, where the
source and the target domain completely share the class of their samples. However, in practice, a target domain can contain samples of classes
that are not shared by the source domain. We call such classes the “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. This approach allows to extract features that
separate unknown target from known target samples. During training,
we assign two options to the feature generator: aligning target samples
with source known ones or rejecting them as unknown target ones. Our
method was extensively evaluated and outperformed other methods with
a large margin in most settings