The goal of domain generalization (DG) is to enhance the generalization capability of the
model learned from a source domain to other unseen domains. The recently developed …

Learning robust models that generalize well under changes in the data distribution is critical
for real-world applications. To this end, there has been a growing surge of interest to learn …

Deep learning has achieved tremendous success in recent years, but most of these
successes are built on an independent and identically distributed (IID) assumption. This …

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 Machine learning society has witnessed the emergence of a myriad of Out-of-
Distribution (OoD) algorithms, which address the distribution shift between the training and …

Abstract Out-of-distribution (OOD) generalization is a critical ability for deep learning models
in many real-world scenarios including healthcare and autonomous vehicles. Recently …

Machine learning models often fail to generalize well under distributional shifts.
Understanding and overcoming these failures have led to a research field of Out-of …

Automated seizure detection is of great importance to epilepsy diagnosis and treatment. An
emerging method used in seizure detection, stereoelectroencephalography (SEEG), can …

While machine learning models rapidly advance the state-of-the-art on various real-world
tasks, out-of-domain (OOD) generalization remains a challenging problem given the …

Abstract Compositional Zero-Shot Learning (CZSL) aims to recognize novel compositions
using knowledge learned from seen attribute-object compositions in the training set …