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arXiv · 1908.00704

Greedy AutoAugment

Alireza Naghizadeh·Mohammadsajad Abavisani·Dimitris N. Metaxas
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Abstract

A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space. In this paper, we propose Greedy AutoAugment as a highly efficient search algorithm to find the best augmentation policies. We use a greedy approach to reduce the exponential growth of the number of possible trials to linear growth. The Greedy Search also helps us to lead the search towards the sub-policies with better results, which eventually helps to increase the accuracy. The proposed method can be used as a reliable addition to the current artifitial neural networks. Our experiments on four datasets (Tiny ImageNet, CIFAR-10, CIFAR-100, and SVHN) show that Greedy AutoAugment provides better accuracy, while using 360 times fewer computational resources.

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Alireza Naghizadeh, Mohammadsajad Abavisani, Dimitris N. Metaxas. 2019-08-02. Greedy AutoAugment. https://arxiv.org/abs/1908.00704

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