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 …

Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …

Calibration of neural networks is a topical problem that is becoming more and more
important as neural networks increasingly underpin real-world applications. The problem is …

As AI systems have obtained significant performance to be deployed widely in our daily live
and human society, people both enjoy the benefits brought by these technologies and suffer …

Successful deployment of artificial intelligence (AI) in various settings has led to numerous
positive outcomes for individuals and society. However, AI systems have also been shown to …

In response to the problems of inaccurate feature alignment, loss of source domain
information, imbalanced sample distribution, and biased class decision boundaries in …

Abstract Domain adaptation, the important branch of transfer learning, aims to solve the
problem that the performance of traditional machine learning algorithms drops sharply when …