Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Deep learning has become the method of choice to tackle real-world problems in different
domains, partly because of its ability to learn from data and achieve impressive performance …

Unsupervised domain adaptation (UDA) via deep learning has attracted appealing attention
for tackling domain-shift problems caused by distribution discrepancy across different …

Source-Free domain adaptation transits the source-trained model towards target domain
without exposing the source data, trying to dispel these concerns about data privacy and …

To ease the burden of labeling, unsupervised domain adaptation (UDA) aims to transfer
knowledge in previous and related labeled datasets (sources) to a new unlabeled dataset …

Black-box unsupervised domain adaptation (UDA) learns with source predictions of target
data without accessing either source data or source models during training, and it has clear …

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an
attractive technique in many real-world applications, though it also brings great challenges …

In the field of Transfer Learning, Source-Free Unsupervised Domain Adaptation (SFUDA)
emerges as a practical and novel task that enables a pre-trained model to adapt to a new …

A brain–computer interface (BCI) establishes a direct communication pathway between the
human brain and a computer. It has been widely used in medical diagnosis, rehabilitation …

Source-free unsupervised domain adaptation is one class of practical deep learning
methods which generalize in the target domain without transferring data from source …