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arXiv · 2106.08756

Optimizing Data Augmentation Policy Through Random Unidimensional Search

Xiaomeng Dong·Michael Potter·Gaurav Kumar·Yun-Chan Tsai·V. Ratna Saripalli·Theodore Trafalis
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Abstract

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

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Xiaomeng Dong, Michael Potter, Gaurav Kumar, Yun-Chan Tsai, V. Ratna Saripalli, Theodore Trafalis. 2021-06-16. Optimizing Data Augmentation Policy Through Random Unidimensional Search. https://arxiv.org/abs/2106.08756

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