Multi-source domain adaptation (MSDA) methods aim to transfer knowledge from multiple
labeled source domains to an unlabeled target domain. Although current methods achieve …

Self-training and contrastive learning have emerged as leading techniques for incorporating
unlabeled data, both under distribution shift (unsupervised domain adaptation) and when it …

Despite the emergence of principled methods for domain adaptation under label shift, their
sensitivity to shifts in class conditional distributions is precariously under explored …

We study the domain adaptation problem with label shift in this work. Under the label shift
context, the marginal distribution of the label varies across the training and testing datasets …

This paper focuses on supervised and unsupervised online label shift, where the class
marginals $ Q (y) $ variesbut the class-conditionals $ Q (x| y) $ remain invariant. In the …

Image-language models with prompt learning have shown remarkable advances in
numerous downstream vision tasks. Nevertheless conventional prompt learning methods …

Deep learning, despite its broad applicability, grapples with robustness challenges in real-
world applications, especially when training and test distributions differ. Reasons for the …