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 …

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 …

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

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

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 …

Conventional zero-shot learning (ZSL) methods generally learn an embedding, eg, visual-
semantic mapping, to handle the unseen visual samples via an indirect manner. In this …

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 aims to leverage knowledge from a well-labeled source domain to a
poorly labeled target domain. A majority of existing works transfer the knowledge at either …

It is widely acknowledged that the success of deep learning is built upon large-scale training
data and tremendous computing power. However, the data and computing power are not …

Domain adaptation aims to exploit the supervision knowledge in a source domain for
learning prediction models in a target domain. In this article, we propose a novel …