Open-set domain adaptation is a developing and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent
advances in representation learning give rise to distance-based OOD detection, where …

Deep neural networks usually depend on substantial labeled data and suffer from poor
generalization to new domains. Domain adaptation can be used to resolve these issues …

Abstract Open-set Domain Adaptation (OSDA) aims to recognize classes in the target
domain that are seen in the source domain while rejecting other unseen target-exclusive …

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

Universal multi-source domain adaptation (UniMDA) aims to transfer the knowledge from
multiple labeled source domains to an unlabeled target domain without constraints on the …

Multisource domain adaptation (MDA) is committed to mining and extracting data
concerning target tasks from several source domains. Many recent studies have focused on …

In an effort to reduce annotation costs in action recognition, unsupervised video domain
adaptation methods have been proposed that aim to adapt a predictive model from a …

Convolutional neural networks (CNNs) can learn directly from raw data, resulting in
exceptional performance across various research areas. However, factors present in non …

Domain adaptation mitigates the decline in performance that occurs when models are
utilized in a target domain. Models designed for a limited range of categories struggle to …