Traditional machine learning paradigms are based on the assumption that both training and
test data follow the same statistical pattern, which is mathematically referred to as …

The main challenge in domain generalization (DG) is to handle the distribution shift problem
that lies between the training and test data. Recent studies suggest that test-time training …

Generalizable face anti-spoofing (FAS) based on domain generalization (DG) has gained
growing attention due to its robustness in real-world applications. However, these DG …

Unsupervised domain adaptation aims to transfer knowledge from a fully-labeled source
domain to an unlabeled target domain. However, in real-world scenarios, providing …

Cross-domain image retrieval has been extensively studied due to its high practical value. In
recently proposed unsupervised cross-domain image retrieval methods, efforts are taken to …

In recent years, machine learning and deep neural networks applications have experienced
a remarkable surge in the field of physics, with optics being no exception. This tutorial aims …

Abstract Domain Generalization (DG), designed to enhance out-of-distribution (OOD)
generalization, is all about learning invariance against domain shifts utilizing sufficient …

The robustness of machine learning algorithms to distributions shift is primarily discussed in
the context of supervised learning (SL). As such, there is a lack of insight on the robustness …

Abstract Domain generalization aims to solve the challenge of Out-of-Distribution (OOD)
generalization by leveraging common knowledge learned from multiple training domains to …

Abstract Domain Generalization (DG) has achieved great success in generalizing
knowledge from source domains to unseen target domains. However, current DG methods …