Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Data-driven machine learning (ML) is promoted as one potential technology to be used in
next-generation wireless systems. This led to a large body of research work that applies ML …

Domain generalization (DG) aims at learning generalizable models under distribution shifts
to avoid redundantly overfitting massive training data. Previous works with complex loss …

Domain generalization (DG) aims at solving distribution shift problems in various scenes.
Existing approaches are based on Convolution Neural Networks (CNNs) or Vision …

Medical Image Analysis (MedIA) has become an essential tool in medicine and healthcare,
aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in …

Abstract Domain generalization aims to improve the generalization capability of machine
learning systems to out-of-distribution (OOD) data. Existing domain generalization …

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

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 neural networks are likely to fail when the test data is corrupted in real-world
deployment (eg, blur, weather, etc.). Test-time optimization is an effective way that adapts …

Domain generalization (DG) aims at learning a model on source domains to well generalize
on the unseen target domain. Although it has achieved great success, most of the existing …