  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.
