Abstract Universal Domain Adaptation (UniDA) is a technology that enables the intelligent
model to transfer knowledge learned from labeled source domains to related but unlabeled …

Abstract Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source
domain to a target domain without any constraints on label sets. Since both domains may …

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to
transfer the knowledge from source to target under unaligned label space. The main …

Source-free universal domain adaptation (SFUniDA) is a challenging yet practical problem
that adapts the source model to the target domain in the presence of distribution and …

Abstract Domain adaptation aims to transfer knowledge in the presence of the domain gap.
Existing domain adaptation methods rely on rich prior knowledge about the relationship …

Universal domain adaptation (UniDA) is a practical but challenging problem, in which
information about the relation between the source and the target domains is not given for …

To transfer the knowledge learned from a labeled source domain to an unlabeled target
domain, many studies have worked on universal domain adaptation (UniDA), where there is …

Many existing approaches for unsupervised domain adaptation (UDA) focus on adapting
under only data distribution shift and offer limited success under additional cross-domain …

Universal domain adaptation aims to transfer the knowledge of common classes from the
source domain to the target domain without any prior knowledge on the label set, which …

As a more practical setting for unsupervised domain adaptation, Universal Domain
Adaptation (UDA) is recently introduced, where the structure of the target label set is …