  We propose the use of fractals as a means of efficient data augmentation.
Specifically, we employ plasma fractals for adapting global image augmentation
transformations into continuous local transforms. We formulate the diamond
square algorithm as a cascade of simple convolution operations allowing
efficient computation of plasma fractals on the GPU. We present the TorMentor
image augmentation framework that is totally modular and deterministic across
images and point-clouds. All image augmentation operations can be combined
through pipelining and random branching to form flow networks of arbitrary
width and depth. We demonstrate the efficiency of the proposed approach with
experiments on document image segmentation (binarization) with the DIBCO
datasets. The proposed approach demonstrates superior performance to
traditional image augmentation techniques. Finally, we use extended synthetic
binary text images in a self-supervision regiment and outperform the same model
when trained with limited data and simple extensions.
