Domain adaptation assumes that samples from source and target domains are freely
accessible during a training phase. However, such an assumption is rarely plausible in the …

Domain adaptation aims to leverage a label-rich domain (the source domain) to help model
learning in a label-scarce domain (the target domain). Most domain adaptation methods …

Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in
real-world scenarios, which relaxes the assumption in conventional Unsupervised Domain …

Abstract The field of Active Domain Adaptation (ADA) has been investigating ways to close
the performance gap between supervised and unsupervised learning settings. Previous …

Partial domain adaptation (PDA) assumes that target domain class label set is a subset of
that of source domain, while this problem setting is close to the actual scenario. At present …

Traditional 3D point cloud classification tasks focus on training a classifier in the closed-set
scenario, where training and test data have the same label set and the same data …

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

Nuclear norm maximization has shown the power to enhance the transferability of
unsupervised domain adaptation model (UDA) in an empirical scheme. In this paper, we …

Physical systems are extending their monitoring capacities to edge areas with low-cost, low-
power sensors and advanced data mining and machine learning techniques. However, new …

Domain adaptation (DA) becomes an up-and-coming technique to address the insufficient or
no annotation issue by exploiting external source knowledge. Existing DA algorithms mainly …