Out-of-domain (OOD) generalization is a significant challenge for machine
learning models. Many techniques have been proposed to overcome this challenge,
often focused on learning models with certain invariance properties. In this
work, we draw a link between OOD performance and model calibration, arguing
that calibration across multiple domains can be viewed as a special case of an
invariant representation leading to better OOD generalization. Specifically, we
show that under certain conditions, models which achieve \emph{multi-domain
calibration} are provably free of spurious correlations. This leads us to
propose multi-domain calibration as a measurable and trainable surrogate for
the OOD performance of a classifier. We therefore introduce methods that are
easy to apply and allow practitioners to improve multi-domain calibration by
training or modifying an existing model, leading to better performance on
unseen domains. Using four datasets from the recently proposed WILDS OOD
benchmark, as well as the Colored MNIST dataset, we demonstrate that training
or tuning models so they are calibrated across multiple domains leads to
significantly improved performance on unseen test domains. We believe this
intriguing connection between calibration and OOD generalization is promising
from both a practical and theoretical point of view.

本文提出了一种新颖的多域校准机制，该机制使分类器能够在未知领域中实现良好的泛化性能，实验证明通过训练或修改模型实现多域校准可以显著提高模型在未知测试域上的性能。