AFN：基于编码器-解码器框架的自适应融合归一化
AFN: Adaptive Fusion Normalization via an Encoder-Decoder Framework
AI总结：
本文提出自适应融合归一化AFN，通过编码器-解码器框架统一多种归一化过程，并在领域泛化和图像分类任务中验证其优于已有归一化方法。
摘要：
深度学习的成功离不开归一化层。研究者们已经提出了多种归一化函数，每种函数都有其优点和缺点。为此，人们致力于设计一种统一的归一化函数，以结合所有归一化过程并减轻它们的弱点。我们也提出了一种新的归一化函数，称为自适应融合归一化（Adaptive Fusion Normalization）。通过实验，我们证明了AFN在领域泛化和图像分类任务中优于以往的归一化技术。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
