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.

数据扩充中的主要问题是确保生成的新样本覆盖搜索空间。这是一个具有挑战性的问题，需要探索数据增强策略以确保其在覆盖搜索空间方面的有效性。在本文中，我们提出贪婪自动增强作为一种高效的搜索算法，以找到最佳的增强策略。我们使用贪婪方法将可能试验的指数增长减少为线性增长。贪婪搜索还可以帮助我们将搜索结果引向具有更好结果的子策略，从而最终有助于提高准确性。所提出的方法可以用作当前人工神经网络的可靠补充。我们在四个数据集（Tiny ImageNet，CIFAR-10，CIFAR-100，