Despite the excellent capabilities of machine learning algorithms, their performance
deteriorates when the distribution of test data differs from the distribution of training data. In …

Abstract Domain adaptation (DA) plays a crucial role in achieving subject-independent
performance in Brain-Computer Interface (BCI). However, previous studies have primarily …

The existing sea–land clutter classification task of sky-wave over-the-horizon-radar (OTHR)
assumes that the training data and test data are drawn from the same probability distribution …

Synthetic aperture radar (SAR) target recognition is a fundamental task in SAR image
interpretation, which has made tremendous progress with the advancement of artificial …

Abstract Deep Metric Learning (DML) plays an important role in modern computer vision
research, where we learn a distance metric for a set of image representations. Recent DML …

Low-dose computed tomography (LDCT) image reconstruction techniques can reduce
patient radiation exposure while maintaining acceptable imaging quality. Deep learning …

Abstract Domain adaptation for semantic segmentation requires pixel-level knowledge
transfer from a labeled source domain to an unlabeled target domain. Existing approaches …

Machine learning algorithms have revolutionized different fields, including natural language
processing, computer vision, signal processing, and medical data processing. Despite the …

Traditional calibration transfer (CT) methods usually fail to adapt the source domain model
to the target domain because of changes associated with the instrument, detection …

In numerous real-world applications, obtaining labeled data for a specific deep learning task
can be prohibitively expensive. We present an innovative framework for unsupervised …