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 generalization (DG) is essentially an out-of-distribution problem, aiming to
generalize the knowledge learned from multiple source domains to an unseen target …

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

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …

We investigate a practical domain adaptation task, called source-free domain adaptation
(SFUDA), where the source pretrained model is adapted to the target domain without access …

Unsupervised domain adaptation has recently emerged as an effective paradigm for
generalizing deep neural networks to new target domains. However, there is still enormous …

Conventional machine learning algorithms suffer the problem that the model trained on
existing data fails to generalize well to the data sampled from other distributions. To tackle …

Abstract Domain adaptation (DA) paves the way for label annotation and dataset bias issues
by the knowledge transfer from a label-rich source domain to a related but unlabeled target …

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 …

In unsupervised domain adaptation (UDA), directly adapting from the source to the target
domain usually suffers significant discrepancies and leads to insufficient alignment. Thus …