Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Adversarial learning has achieved remarkable performances for unsupervised domain
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …

We propose a simple but effective source-free domain adaptation (SFDA) method. Treating
SFDA as an unsupervised clustering problem and following the intuition that local neighbors …

Unsupervised domain adaptation (UDA) aims to transfer knowledge from a related but
different well-labeled source domain to a new unlabeled target domain. Most existing UDA …

Abstract Domain Adaptation aims to transfer the knowledge learned from a labeled source
domain to an unlabeled target domain whose data distributions are different. However, the …

Unsupervised domain adaptation (UDA) methods for learning domain invariant
representations have achieved remarkable progress. However, most of the studies were …

Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant
representations to narrow the domain shift, which are empirically effective but theoretically …

Unsupervised domain adaptation (UDA) approaches focus on adapting models trained on a
labeled source domain to an unlabeled target domain. In contrast to UDA, source-free …

Source-free domain adaptation (SFDA) newly emerges to transfer the relevant knowledge of
a well-trained source model to an unlabeled target domain, which is critical in various …