  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.
