Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

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) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

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 …

Conventional domain adaptation (DA) techniques aim to improve domain transferability by
learning domain-invariant representations; while concurrently preserving the task …

Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target
data set by only using a pre-trained source model. However, the absence of the source data …

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

This work focuses on a practical knowledge transfer task defined as Source-Free
Unsupervised Domain Adaptation (SFUDA), where only a well-trained source model and …

Abstract Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence
of both covariate and label shifts. Recently Source-free Universal Domain Adaptation (SF …