  The paper discusses regularization properties of artificial data for deep
learning. Artificial datasets allow to train neural networks in the case of a
real data shortage. It is demonstrated that the artificial data generation
process, described as injecting noise to high-level features, bears several
similarities to existing regularization methods for deep neural networks. One
can treat this property of artificial data as a kind of "deep" regularization.
It is thus possible to regularize hidden layers of the network by generating
the training data in a certain way.
