Optimizing Data Augmentation Policy Through Random Unidimensional Search
This reduces computational overhead for deep learning practitioners, though it is incremental as it builds on existing automation efforts.
The paper tackles the problem of high computational cost in finding optimal data augmentation strategies, achieving equivalent performance with only 6 model trainings compared to 100 in prior methods.
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