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 …

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

Surface defect recognition using Deep Learning based computer vision techniques is an
important task in industrial manufacturing. However, surface images have different …

Unsupervised domain adaptation addresses the problem of transferring knowledge from a
well-labeled source domain to an unlabeled target domain where the two domains have …

The problem of generalizing deep neural networks from multiple source domains to a target
one is studied under two settings: When unlabeled target data is available, it is a multi …

Recent studies have shown that deep domain adaptation (DA) techniques have good
performance on cross-domain hyperspectral image (HSI) classification problems. However …

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 …

Traditional remote sensing (RS) image classification methods heavily rely on labeled
samples for model training. When labeled samples are unavailable or labeled samples have …

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 …

A common strategy adopted by existing state-of-the-art unsupervised domain adaptation
(UDA) methods is to employ two classifiers to identify the misaligned local regions between …