Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

Supervised learning aims to train a classifier under the assumption that training and test
data are from the same distribution. To ease the above assumption, researchers have …

Unsupervised domain adaptation without accessing expensive annotation processes of
target data has achieved remarkable successes in semantic segmentation. However, most …

Data-driven machinery fault diagnosis methods have been successfully developed in the
past decades. However, the cross-domain diagnostic problems have not been well …

Dominance-based rough approximation discovers inconsistencies from ordered criteria and
satisfies the requirement of the dominance principle between single-valued domains of …

Federated learning-based semantic segmentation (FSS) has drawn widespread attention
via decentralized training on local clients. However, most FSS models assume categories …

In this paper, we investigate Universal Domain Adaptation (UniDA) problem, which aims to
transfer the knowledge from source to target under unaligned label space. The main …

Unsupervised domain adaptation has attracted appealing academic attentions by
transferring knowledge from labeled source domain to unlabeled target domain. However …

Medical image segmentation, which is a complex and fundamental step in medical image
processing, can help doctors make more precise decisions on patient diagnosis. Although …