  Although data augmentation is a powerful technique for improving the
performance of image classification tasks, it is difficult to identify the best
augmentation policy. The optimal augmentation policy, which is the latent
variable, cannot be directly observed. To address this problem, this study
proposes $\textit{LatentAugment}$, which estimates the latent probability of
optimal augmentation. The proposed method is appealing in that it can
dynamically optimize the augmentation strategies for each input and model
parameter in learning iterations. Theoretical analysis shows that LatentAugment
is a general model that includes other augmentation methods as special cases,
and it is simple and computationally efficient in comparison with existing
augmentation methods. Experimental results show that the proposed LatentAugment
has higher test accuracy than previous augmentation methods on the CIFAR-10,
CIFAR-100, SVHN, and ImageNet datasets.
