A popular method to reduce the training time of deep neural networks is to
normalize activations at each layer. Although various normalization schemes
have been proposed, they all follow a common theme: normalize across spatial
dimensions and discard the extracted statistics. In this paper, we propose an
alternative normalization method that noticeably departs from this convention
and normalizes exclusively across channels. We argue that the channel dimension
is naturally appealing as it allows us to extract the first and second moments
of features extracted at a particular image position. These moments capture
structural information about the input image and extracted features, which
opens a new avenue along which a network can benefit from feature
normalization: Instead of disregarding the normalization constants, we propose
to re-inject them into later layers to preserve or transfer structural
information in generative networks. Codes are available at
this https URL

本文提出了一种基于通道归一化的激活值归一化方法，通过提取特征在图像位置的一阶和二阶矩来捕获输入图像和抽取特征的结构信息，并将归一化常数重新注入后续层以保留或转移结构信息在生成网络中。