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 …

Image restoration (IR) has been an indispensable and challenging task in the low-level
vision field, which strives to improve the subjective quality of images distorted by various …

Abstract Machine learning algorithms typically assume that training and test examples are
drawn from the same distribution. However, distribution shift is a common problem in real …

A fundamental challenge for machine learning models is generalizing to out-of-distribution
(OOD) data, in part due to spurious correlations. To tackle this challenge, we first formalize …

Recently, various fault diagnosis methods based on domain adaptation (DA) have been
explored to solve the problem of discrepancy between the source and target domains …

Abstract Domain generalization is a ubiquitous challenge for machine learning in
healthcare. Model performance in real-world conditions might be lower than expected …

Deep learning has achieved tremendous success with independent and identically
distributed (iid) data. However, the performance of neural networks often degenerates …

Abstract Domain generalization (DG) aims to help models trained on a set of source
domains generalize better on unseen target domains. The performances of current DG …

Abstract Domain Generalization (DG) techniques have emerged as a popular approach to
address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing …

Abstract Face Anti-Spoofing (FAS) is pivotal in safeguarding facial recognition systems
against presentation attacks. While domain generalization (DG) methods have been …