Normalization is a pre-processing step that converts the data into a more
usable representation. As part of the deep neural networks (DNNs), the batch
normalization (BN) technique uses normalization to address the problem of
internal covariate shift. It can be packaged as general modules, which have
been extensively integrated into various DNNs, to stabilize and accelerate
training, presumably leading to improved generalization. However, the effect of
BN is dependent on the mini-batch size and it does not take into account any
groups or clusters that may exist in the dataset when estimating population
statistics. This study proposes a new normalization technique, called context
normalization, for image data. This approach adjusts the scaling of features
based on the characteristics of each sample, which improves the model's
convergence speed and performance by adapting the data values to the context of
the target task. The effectiveness of context normalization is demonstrated on
various datasets, and its performance is compared to other standard
normalization techniques.

本文提出一种称为上下文归一化的新的归一化技术，用于图像数据。这种方法基于每个样本的特征调整特征的缩放，通过使数据值适应目标任务的上下文来提高模型的收敛速度和性能。在各种数据集上展示了上下文归一化的有效性，并将其性能与其他标准归一化技术进行了比较。

稳健图像分类的上下文归一化

Context Normalization for Robust Image Classification

We develop a deep architecture to learn to find good correspondences for
wide-baseline stereo. Given a set of putative sparse matches and the camera
intrinsics, we train our network in an end-to-end fashion to label the
correspondences as inliers or outliers, while simultaneously using them to
recover the relative pose, as encoded by the essential matrix. Our architecture
is based on a multi-layer perceptron operating on pixel coordinates rather than
directly on the image, and is thus simple and small. We introduce a novel
normalization technique, called Context Normalization, which allows us to
process each data point separately while imbuing it with global information,
and also makes the network invariant to the order of the correspondences. Our
experiments on multiple challenging datasets demonstrate that our method is
able to drastically improve the state of the art with little training data.

该研究使用多层感知器来优化广基准立体匹配的好匹配点，同时恢复相对位置关系，该网络基于像素坐标运算而非直接图像处理，提出了一种称为上下文规范化的新型正则化技术，使得可以单独处理每个数据点并赋予全局信息。在多个数据集的实验中，该方法使用少量训练数据就能显著提高现有技术的水平。