Multisource domain adaptation (MDA) aims to transfer knowledge from multiple labeled
source domains to an unlabeled target domain. However, the severe intradomain and …

Unsupervised domain adaptation (UDA) has emerged as a powerful solution for the domain
shift problem via transferring the knowledge from a labeled source domain to a shifted …

Most efforts in unsupervised domain adaptation (UDA) focus on learning the domain-
invariant representations between the two domains. However, such representations may still …

Abstract Multi-Source Domain Adaptation (MSDA) aims at training a classification model that
achieves small target error, by leveraging labeled data from multiple source domains and …

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a related but
inconsistently distributed source domain for target model training. The challenge of …

In the realm of object detection, traditional domain adaptive object detection (DAOD)
methods assume that source and target data completely share one identical class space …

Prototypical contrastive learning (PCL) has been widely used to learn class-wise domain-
invariant features recently. These methods are based on the assumption that the prototypes …

Abstract Domain adaptation is an important topic due to its capability in transferring
knowledge from source domain to target domain. However, many existing domain …

Introduction Motor imagery electroencephalograph (MI-EEG) has attracted great attention in
constructing non-invasive brain-computer interfaces (BCIs) due to its low-cost and …

By using unsupervised domain adaptation (UDA), knowledge is transferred from a label-rich
source domain to a target domain that contains relevant information but has no labels. Most …