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：深度学习成功的关键因素之一在于归一化层。研究者提出了多种归一化函数，每种函数均兼具优势与不足。为此，已有研究尝试设计统一归一化函数，旨在融合所有归一化流程并弥补其缺陷。我们提出了一种名为"自适应融合归一化"（AFN）的新归一化函数。通过实验表明，AFN在领域泛化与图像分类任务中均优于现有归一化技术。