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Greedy AutoAugment.
- Source :
-
Pattern Recognition Letters . Oct2020, Vol. 138, p624-630. 7p. - Publication Year :
- 2020
-
Abstract
- • The greedy AutoAugment is proposed to find the best augmentation policies. • It reduces the exponential growth of the number of possible trials to linear growth. • The algorithm is computationally efficient and can concatenate many sub-policies. • The greedy approach helps the algorithm to increase the accuracy of the network. 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 138
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 146478436
- Full Text :
- https://doi.org/10.1016/j.patrec.2020.08.024