Automating Augmentation Through Random Unidimensional Search

It is no secret amongst deep learning researchers that finding the right data augmentation strategy during training can mean the difference between a state-of-the-art result and a run-of-the-mill ranking. 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 even better performance in just 7: with Random Unidimensional Augmentation. Source code is available at https://github.com/fastestimator/RUA