Domain adaptation has become a prominent problem setting in machine learning and
related fields. This review asks the question: How can a classifier learn from a source …

The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

Recommender systems provide personalized service support to users by learning their
previous behaviors and predicting their current preferences for particular products. Artificial …

Abstract Remaining Useful Life (RUL) prediction is a key task of Condition-based
Maintenance (CBM). The massive data collected from multiple sensors enables monitoring …

This work studies the bias issue of pseudo-labeling, a natural phenomenon that widely
occurs but often overlooked by prior research. Pseudo-labels are generated when a …

Transfer learning aims to provide a framework to utilize previously-acquired knowledge to
solve new but similar problems much more quickly and effectively. In contrast to classical …

People with diabetes are at risk of develo** an eye disease called diabetic retinopathy
(DR). This disease occurs when high blood glucose levels cause damage to blood vessels …

Transfer learning is established as an effective technology in computer vision for leveraging
rich labeled data in the source domain to build an accurate classifier for the target domain …

In the problem of domain generalization (DG), there are labeled training data sets from
several related prediction problems, and the goal is to make accurate predictions on future …

Detecting malicious software (malware) is important for computer security. Among the
different types of malware, zero-day malware is problematic because it cannot be removed …