Faster and Smarter AutoAugment: Augmentation Policy Search Based on Dynamic Data-Clustering

Abstract

Data augmentation tuned to datasets and tasks has had great success in various AI applications, such as computer vision, natural language processing, autonomous driving, and bioinformatics. However, most of the specific parameter-based augmentation strategies are inefficient in finding suitable augmentation parameters to improve the model performance whenever the dataset changes. We introduce a dynamic data augmentation strategy called Faster and Smarter AutoAugment (FSAA) that separates the data augmentation method through the initial policy search result. Based on our policy branching principle, the augmentation policy dynamically clusters data points within the dataset according to the degree of performance change at the data split stage. With extensive experimentation on various datasets for image recognition task, we show that FSAA dramatically reduces the GPU computation cost, a problem in the existing automatic data augmentation strategies, by up to over 90%. It also ensures diversity and generalization of the dataset augmentation by searching for more diverse policies than existing autonomous methods in less time, resulting in consistent performance gains and robustness across multiple models.

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Reproductions