You may joking about it, but ML researchers use all kinds of strategies like this one in order to perform what they call _data augmentation_.

In particular for computer vision tasks, creating a perturbed variant of your input images (slightly warped, flipped, mirror, what have you...) can do wonders for the generalization performance.