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 …

Deep neural networks (DNNs) usually fail to generalize well to outside of distribution (OOD)
data, especially in the extreme case of single domain generalization (single-DG) that …

Abstract Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence
of both covariate and label shifts. Recently Source-free Universal Domain Adaptation (SF …

Abstract 3D point cloud segmentation has received significant interest for its growing
applications. However, the generalization ability of models suffers in dynamic scenarios due …

Domain generalization (DG) aims to learn a model that generalizes well to an unseen test
distribution. Mainstream methods follow the domain-invariant representational learning …

Deep learning has achieved remarkable progress in various applications heightening the
importance of safeguarding the intellectual property (IP) of well-trained models. It entails not …

Universal Domain Adaptation (UniDA) aims to distinguish common and private classes
between the source and target domains where domain shift exists. Recently, due to more …