Single domain generalization aims to learn a model that performs well on many
unseen domains with only one domain data for training. Existing works focus on
studying the adversarial domain augmentation (ADA) to improve the model's
generalization capability. The impact on domain generalization of the
statistics of normalization layers is still underinvestigated. In this paper,
we propose a generic normalization approach, adaptive standardization and
rescaling normalization (ASR-Norm), to complement the missing part in previous
works. ASR-Norm learns both the standardization and rescaling statistics via
neural networks. This new form of normalization can be viewed as a generic form
of the traditional normalizations. When trained with ADA, the statistics in
ASR-Norm are learned to be adaptive to the data coming from different domains,
and hence improves the model generalization performance across domains,
especially on the target domain with large discrepancy from the source domain.
The experimental results show that ASR-Norm can bring consistent improvement to
the state-of-the-art ADA approaches by 1.6%, 2.7%, and 6.3% averagely on the
Digits, CIFAR-10-C, and PACS benchmarks, respectively. As a generic tool, the
improvement introduced by ASR-Norm is agnostic to the choice of ADA methods.

本文提出了一种通用的标准化方法 — 自适应标准化和重新缩放标准化（ASR-Norm），通过神经网络学习标准化和重新缩放的统计数据，可以进一步提高单域泛化模型的范化能力，特别是在源域与目标域差异较大的情况下，该方法在 Digits、CIFAR-10-C 和 PACS 基准测试中与 ADA 方法相比可平均提高 1.6%、2.7% 和 6.3%。