Efficient search for data augmentation policies by applying affinity and diversity

Abstract

Numerous methods exist for data augmentation, each with its own hyperparameters. It is necessary to search for an appropriate data augmentation policy for each task, but the conventional search method using validation data requires a large computational cost. In this study, we propose a new metric, which incorporates the data augmentation metrics called Affinity and Diversity to select an appropriate data augmentation policy in a short training time. Experimental results on several datasets show that the proposed method can efficiently search for a data augmentation policy with small computational cost and high accuracy.