Myocardial infarction is a common cardiovascular disorder caused by prolonged ischemia,
and early diagnosis of myocardial infarction (MI) is critical for lifesaving. ECG is a simple and …

Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

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 …

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 …

The recent advances in deep transfer learning reveal that adversarial learning can be
embedded into deep networks to learn more transferable features to reduce the distribution …

Transfer learning aims to learn robust classifiers for the target domain by leveraging
knowledge from a source domain. Since the source and the target domains are usually from …

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

Unsupervised domain adaptation is critical to many real-world applications where label
information is unavailable in the target domain. In general, without further assumptions, the …

Recently, extensive researches have been proposed to address the UDA problem, which
aims to learn transferrable models for the unlabeled target domain. Among them, the optimal …

Recently, domain adaptation has received extensive attention for solving intelligent fault
diagnosis problems. It aims to reduce the distribution discrepancy between the source …