The success of deep learning is inseparable from normalization layers. Researchers have proposed various normalization functions, and each of them has both advantages and disadvantages. In response, efforts have been made to design a unified normalization function that combines all normalization procedures and mitigates their weaknesses. We also proposed a new normalization function called Adaptive Fusion Normalization. Through experiments, we demonstrate AFN outperforms the previous normalization techniques in domain generalization and image classification tasks.

翻译：深度学习方法的成功离不开归一化层。研究者们提出了多种归一化函数，每种方法各有优劣。为此，学界致力于设计一种融合所有归一化过程并弥补其缺陷的统一归一化函数。我们提出了一种名为自适应融合归一化的新型归一化函数。实验证明，在领域泛化和图像分类任务中，AFN的性能优于现有归一化技术。