  It is no secret amongst deep learning researchers that finding the optimal
data augmentation strategy during training can mean the difference between
state-of-the-art performance and a run-of-the-mill result. To that end, the
community has seen many efforts to automate the process of finding the perfect
augmentation procedure for any task at hand. Unfortunately, even recent
cutting-edge methods bring massive computational overhead, requiring as many as
100 full model trainings to settle on an ideal configuration. We show how to
achieve equivalent performance using just 6 trainings with Random
Unidimensional Augmentation. Source code is available at
https://github.com/fastestimator/RUA/tree/v1.0
