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：深度学习取得的成功离不开归一化层。研究者已提出多种归一化函数，每种方法均兼具优缺点。为此，相关工作致力于设计统一的归一化函数，以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的新型归一化函数。实验证明，AFN在领域泛化和图像分类任务中均优于先前的归一化技术。