Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

State of charge (SOC) and state of health (SOH) estimation play a vital role in battery
management systems (BMSs). Accurate and robust state estimation can prevent Li-ion …

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

Abstract Unsupervised Domain Adaptation (UDA) aims to generalize the knowledge learned
from a well-labeled source domain to an unlabled target domain. Recently, adversarial …

We consider unsupervised domain adaptation (UDA), where labeled data from a source
domain (eg, photos) and unlabeled data from a target domain (eg, sketches) are used to …

In industrial manufacturing systems, failures of machines caused by faults in their key
components greatly influence operational safety and system reliability. Many data-driven …

Abstract Domain adaptation (DA) aims to transfer knowledge from a label-rich but
heterogeneous domain to a label-scare domain, which alleviates the labeling efforts and …

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 …

Conventional machine learning algorithms suffer the problem that the model trained on
existing data fails to generalize well to the data sampled from other distributions. To tackle …

Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and
transferring knowledge from related source domains. In order to learn a discriminative and …