1 research outputs found
Known-class Aware Self-ensemble for Open Set Domain Adaptation
Existing domain adaptation methods generally assume different domains have
the identical label space, which is quite restrict for real-world applications.
In this paper, we focus on a more realistic and challenging case of open set
domain adaptation. Particularly, in open set domain adaptation, we allow the
classes from the source and target domains to be partially overlapped. In this
case, the assumption of conventional distribution alignment does not hold
anymore, due to the different label spaces in two domains. To tackle this
challenge, we propose a new approach coined as Known-class Aware Self-Ensemble
(KASE), which is built upon the recently developed self-ensemble model. In
KASE, we first introduce a Known-class Aware Recognition (KAR) module to
identify the known and unknown classes from the target domain, which is
achieved by encouraging a low cross-entropy for known classes and a high
entropy based on the source data from the unknown class. Then, we develop a
Known-class Aware Adaptation (KAA) module to better adapt from the source
domain to the target by reweighing the adaptation loss based on the likeliness
to belong to known classes of unlabeled target samples as predicted by KAR.
Extensive experiments on multiple benchmark datasets demonstrate the
effectiveness of our approach