Deep Learning: Perturbations and Diversity is All You Need

A generating process can conjure up sufficient complexity that cannot be predicted using the bulk statistics that is observed. The algorithm to conjure up this deceptive distribution is very simple. Take an an existing dataset, perturb it slightly, and continue to maintain specific statistical properties. This is done by randomly selecting a point, add a small perturbation and then validating if the statistics are within targeted bounds. Now repeat these perturbation enough times and you can target different results.