Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Semi-supervised learning (SSL) improves model generalization by leveraging massive
unlabeled data to augment limited labeled samples. However, currently, popular SSL …

For a target task where the labeled data are unavailable, domain adaptation can transfer a
learner from a different source domain. Previous deep domain adaptation methods mainly …

Electroencephalogram (EEG) signal analysis, which is widely used for human-computer
interaction and neurological disease diagnosis, requires a large amount of labeled data for …

With the rapid development of computing technology, wearable devices make it easy to get
access to people's health information. Smart healthcare achieves great success by training …

Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in
one scene, likely fails in classifying by unlabeled data acquired from the other scenes …

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 …

Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging
knowledge from a source domain. Existing methods either attempt to align the cross-domain …

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep
representation learning and plenty of labeled data. However, machines often operate with …