  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.
