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

Label scarcity in a graph is frequently encountered in real-world applications due to the high
cost of data labeling. To this end, semi-supervised domain adaptation (SSDA) on graphs …

Many machine learning models deployed on smart or edge devices experience a phase
where there is a drop in their performance due to the arrival of data from new domains. This …

Unsupervised domain adaptation (UDA) aims to explore the knowledge of labeled source
domain to help training the model of unlabeled target domain. By now, while most existing …

Although unsupervised domain adaptation (UDA) has been extensively studied in remote
sensing image segmentation tasks, most UDA models are designed based on single-target …

The utilisation of domain adaptation methods facilitates the resolution of classification
challenges in an unlabelled target domain by capitalising on the labelled information from …

This paper proposes a novel unsupervised domain adaption (UDA) method based on
contrastive bi-projector (CBP), which can improve the existing UDA methods. It is called …

Unsupervised domain adaptation transfers knowledge from a fully labeled source domain to
a different target domain, where no labeled data are available. Some researchers have …

Domain shift is a common problem in many real-world applications using machine learning
models. Most of the existing solutions are based on supervised and deep-learning models …