Capturing the distribution characteristics of high-dimensional data is very hard. In fact if we could do that well, we might be able to use classic bayesian techniques for many NN problems which would be more principled and interpretable. Any noise one would end up adding by hand is unlikely to introduce the kind of stochasticity that sampling on real data (using minibatches or similar procedures) would. Getting the distribution wrong would likely mean poor generalization.