Domain adaptation (DA) addresses the challenge of distribution discrepancy between the
training and test data, while multi-source domain adaptation (MSDA) is particularly …

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source
domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement …

Multi-source domain adaptation (MSDA) aims to adapt a model trained on multiple labeled
source domains to an unlabeled target domain. Existing MSDA methods primarily focus on …

Graph neural networks (GNNs) have emerged as powerful tools for graph classification
tasks. However, contemporary graph classification methods are predominantly studied in …

In many practical applications, it is often difficult and expensive to obtain large-scale labeled
data to train state-of-the-art deep neural networks. Therefore, transferring the learned …

Adapting a deep learning model to a specific target individual is a challenging facial
expression recognition (FER) task that may be achieved using unsupervised domain …

In realistic scenarios, the effectiveness of Deep Neural Networks is hindered by domain shift,
where discrepancies between training (source) and testing (target) domains lead to poor …

In the remote sensing information interpretation tasks, compared with collecting lots of high-
quality image labels for the target domain, a large amount of labeled remote sensing data …

Combinatorial optimization (CO) on graphs is a classic topic that has been extensively
studied across many scientific and industrial fields. Recently, solving CO problems on …

In this paper, we investigate the multi-source-free domain adaptation (MSFDA), a specific
case of unsupervised domain adaptation where multiple source models are adapted to the …