Why in preprocessing image data, we need to do zero-centered data?

Question

Why in preprocessing image data for a neural network, we need to zero-centered data. Why is this?

Answer 1

Mean-subtraction or zero-centering is a common pre-processing technique that involves subtracting mean from each of the data point to make it zero-centered. Consider a case where inputs to a neuron are all positive or all negative. In that case the gradient calculated during back propagation will either be positive or negative (of the same sign as inputs). And hence parameter updates are only restricted to specific directions which in turn will make it difficult to converge. As a result, the gradient updates go too far in different directions which makes optimization harder. Many algorithms show better performances when the dataset is symmetric (with a zero-mean).