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 …

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 …

In recent years, deep Convolutional Neural Networks (CNNs) have broken all records in
salient object detection. However, training such a deep model requires a large amount of …

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 …

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 …

Given a query instance from one modality (eg, image), cross-modal retrieval aims to find
semantically similar instances from another modality (eg, text). To perform cross-modal …

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 …