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.

翻译：尽管数据增强是提升图像分类任务性能的有效技术，但最优增强策略的识别仍存在困难。作为潜在变量的最优增强策略无法被直接观测。针对这一问题，本研究提出$\textit{LatentAugment}$方法以估计最优增强的潜在概率。该方法的特点在于能根据每个输入与模型参数在学习迭代过程中动态优化增强策略。理论分析表明，LatentAugment是一个通用模型，可将其他增强方法作为特例纳入其中，且与现有增强方法相比具有简洁性和计算高效性。实验结果显示，在CIFAR-10、CIFAR-100、SVHN和ImageNet数据集上，所提出的LatentAugment方法取得了优于以往增强方法的测试准确率。