  Since annotating and curating large datasets is very expensive, there is a
need to transfer the knowledge from existing annotated datasets to unlabelled
data. Data that is relevant for a specific application, however, usually
differs from publicly available datasets since it is sampled from a different
domain. While domain adaptation methods compensate for such a domain shift,
they assume that all categories in the target domain are known and match the
categories in the source domain. Since this assumption is violated under
real-world conditions, we propose an approach for open set domain adaptation
where the target domain contains instances of categories that are not present
in the source domain. The proposed approach achieves state-of-the-art results
on various datasets for image classification and action recognition. Since the
approach can be used for open set and closed set domain adaptation, as well as
unsupervised and semi-supervised domain adaptation, it is a versatile tool for
many applications.
