  Domain adaptation tackles the challenge of generalizing knowledge acquired
from a source domain to a target domain with different data distributions.
Traditional domain adaptation methods presume that the classes in the source
and target domains are identical, which is not always the case in real-world
scenarios. Open-set domain adaptation (OSDA) addresses this limitation by
allowing previously unseen classes in the target domain. Open-set domain
adaptation aims to not only recognize target samples belonging to common
classes shared by source and target domains but also perceive unknown class
samples. We propose a novel framework based on self-paced learning to
distinguish common and unknown class samples precisely, referred to as SPLOS
(self-paced learning for open-set). To utilize unlabeled target samples for
self-paced learning, we generate pseudo labels and design a cross-domain mixup
method tailored for OSDA scenarios. This strategy minimizes the noise from
pseudo labels and ensures our model progressively learns common class features
of the target domain, beginning with simpler examples and advancing to more
complex ones. Furthermore, unlike existing OSDA methods that require manual
hyperparameter $threshold$ tuning to separate common and unknown classes, our
approach self-tunes a suitable threshold, eliminating the need for empirical
tuning during testing. Comprehensive experiments illustrate that our method
consistently achieves superior performance on different benchmarks compared
with various state-of-the-art methods.
