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 …

Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and
execute tasks with human cognitive abilities, engenders significant anticipation and intrigue …

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 …

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 …

The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …

This paper presents a new algorithm for domain generalization (DG),\textit {test-time
template adjuster (T3A)}, aiming to robustify a model to unknown distribution shift. Unlike …

The goal of domain generalization algorithms is to predict well on distributions different from
those seen during training. While a myriad of domain generalization algorithms exist …

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 …

Abstract Domain generalization (DG) methods aim to achieve generalizability to an unseen
target domain by using only training data from the source domains. Although a variety of DG …

Abstract Domain generalization (DG) aims to learn a generalized model to an unseen target
domain using only limited source domains. Previous attempts to DG fail to learn domain …