Generalization to out-of-distribution (OOD) data is a capability natural to humans yet
challenging for machines to reproduce. This is because most learning algorithms strongly …

Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Abstract Machine-learning models for medical tasks can match or surpass the performance
of clinical experts. However, in settings differing from those of the training dataset, the …

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 …

Modern deep neural networks suffer from performance degradation when evaluated on
testing data under different distributions from training data. Domain generalization aims at …

Abstract Federated Domain Generalization (FedDG) attempts to learn a global model in a
privacy-preserving manner that generalizes well to new clients possibly with domain shift …

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 …

This work studies the generalization issue of face anti-spoofing (FAS) models on domain
gaps, such as image resolution, blurriness and sensor variations. Most prior works regard …

With diverse presentation attacks emerging continually, generalizable face anti-spoofing
(FAS) has drawn growing attention. Most existing methods implement domain generalization …

Misinformation has become a pressing issue. Fake media, in both visual and textual forms,
is widespread on the web. While various deepfake detection and text fake news detection …