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I'm wondering about data normalization in CNN, how can we do it for the input images?, what can it add to the model's performance? and what are the main pre-processing techniques before doing the convolutional layer?

Happy to get extra-reading, thanks.

UPDATE:

I'm looking for how to practically normalize the data inputs, I didn't get it! . Additional reading (explanation, papers) will be so helpful.

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Normalizing the data prior to CNN will create more spherical error surfaces which will yield faster convergence of the gradient-based optimization procedure. If this step is skipped, some axes (determined by the eigenvectors of the loss function) will have steeper/flatter structures and consequently, the travel of our optimizer on the surface will get troubled.

You can standardize the images. However, generally, we map the images into 0-1 interval (by dividing to 255 for gray images) so that the values are bounded.

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