Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …

Abstract Unsupervised Domain Adaptation (UDA) can tackle the challenge that
convolutional neural network (CNN)-based approaches for semantic segmentation heavily …

Large-scale labeled training datasets have enabled deep neural networks to excel across a
wide range of benchmark vision tasks. However, in many applications, it is prohibitively …

Intelligent fault diagnosis based on domain adaptation has recently been extensively
researched to promote reliability of safety-critical assets under different working conditions …

Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source
domains to an unlabeled target domain. MDA is a challenging task due to the severe …

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 …

Abstract Multi-Source Domain Adaptation (MSDA) techniques have attracted widespread
attention due to their availability to transfer knowledge from multiple source domains to the …

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

Semantic image segmentation (SiS) plays a fundamental role in a broad variety of computer
vision applications, providing key information for the global understanding of an image. This …