  In this paper, we propose Normalized Convolutional Neural Network(NCNN). NCNN
is more fitted to a convolutional operator than other nomralizaiton methods.
The normalized process is similar to a normalization methods, but NCNN is more
adapative to sliced-inputs and corresponding the convolutional kernel. Therefor
NCNN can be targeted to micro-batch training. Normalizaing of NC is conducted
during convolutional process. In short, NC process is not usual normalization
and can not be realized in deep learning framework optimizing standard
convolution process. Hence we named this method 'Normalized Convolution'. As a
result, NC process has universal property which means NC can be applied to any
AI tasks involving convolution neural layer . Since NC don't need other
normalization layer, NCNN looks like convolutional version of Self Normalizing
Network.(SNN). Among micro-batch trainings, NCNN outperforms other
batch-independent normalization methods. NCNN archives these superiority by
standardizing rows of im2col matrix of inputs, which theoretically smooths the
gradient of loss. The code need to manipulate standard convolution neural
networks step by step. The code is available : https://github.com/kimdongsuk1/
NormalizedCNN.
