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It is no secret amongst deep learning researchers that finding the optimaldata augmentation strategy during training can mean the difference betweenstate-of-the-art performance and a run-of-the-mill result. To that end, thecommunity has seen many efforts to automate the process of finding the perfectaugmentation procedure for any task at hand. Unfortunately, even recentcutting-edge methods bring massive computational overhead, requiring as many as100 full model trainings to settle on an ideal configuration. We show how toachieve equivalent performance using just 6 trainings with RandomUnidimensional Augmentation. Source code is available athttps://github.com/fastestimator/RUA/tree/v1.0

Optimizing Data Augmentation Policy Through Random Unidimensional Search