Unsupervised domain adaptation (UDA) enables the transfer of models trained on source
domains to unlabeled target domains. However, transferring complex time series models …

Modern machine learning models deployed in the wild can encounter both covariate and
semantic shifts, giving rise to the problems of out-of-distribution (OOD) generalization and …

Universal domain adaptation (UniDA) aims to transfer knowledge from the source domain to
the target domain without any prior knowledge about the label set. The challenge lies in how …

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

Motivation The rapid development of spatial transcriptome technologies has enabled
researchers to acquire single-cell-level spatial data at an affordable price. However …

Learning with noisy labels (LNL) poses a significant challenge in training a well-generalized
model while avoiding overfitting to corrupted labels. Recent advances have achieved …

Prompt learning in pretrained visual-language models has shown remarkable flexibility
across various downstream tasks. Leveraging its inherent lightweight nature recent research …

This review article comprehensively delves into the rapidly evolving field of domain
adaptation in computer and robotic vision. It offers a detailed technical analysis of the …

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 …

When encountering the distribution shift between the source (training) and target (test)
domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with …