According to the paper https://arxiv.org/pdf/1502.03167.pdf,
It has been long known (LeCun et al., 1998b; Wiesler & Ney, 2011) that the network training converges faster if its inputs are whitened – i.e., linearly transformed to have zero means and unit variances, and decorrelated.
My question is why would a network learn better from uncorrelated inputs?
My intuition for this is that if your inputs (X,Y) are independent (and in particular uncorrelated), you would expect the same conditional distribution of X no matter the value of Y, thus the data is in some sense more regular. But this is a very handwavy intuition and I would like to understand this claim on a deeper level.