As a branch of transfer learning, domain adaptation leverages useful knowledge from a
source domain to a target domain for solving target tasks. Most of the existing domain …

Unsupervised multidomain adaptation attracts increasing attention as it delivers richer
information when tackling a target task from an unlabeled target domain by leveraging the …

Breast cancer is the most commonly diagnosed cancer worldwide, and early detection is
essential for reducing mortality rates. Digital mammography is currently the best standard for …

Domain adaptation solves image classification problems in the target domain by taking
advantage of the labeled source data and unlabeled target data. Usually, the source and …

Abstract Partial Domain Adaptation (PDA) aims to generalize a classification model from a
labeled source domain to an unlabeled target domain, where the source label space …

Abstract Domain-adaptation technologies have been widely developed for mechanical fault
diagnosis. Most related methods assume the same label space between the source and …

The aim of unsupervised domain adaptation (UDA) is to utilize knowledge from a source
domain to enhance the performance of a given target domain. Due to the lack of accessibility …

In order to accurately diagnose faults across different domains where the fault types are
inconsistent between the source and target domains, a cross-domain fault diagnosis model …

Recently, unsupervised domain adaptation methods have been increasingly applied to
address the domain shift problems in defect detection. However, the effectiveness of most …

Domain adaptation enables the reduction of distribution differences across domains,
allowing for effective knowledge transfer from one domain to a different domain. In recent …