A Progressive Local Variance-guided Strategy for Improving Data Augmentation Reliability

Abstract

Recently, CutMix-based augmentation has emerged as a promising strategy for providing regularization to deep neural networks. However, the randomness in cropping may result in uninformative or non-representative regions being selected, resulting in a synthesized image without the desired features. To address these issues, we propose a simple, flexible, and effective augmentation strategy called Progressive LOcal Variance-guided Mix (PlovMix). PlovMix indicates the effective regions based on the information density distribution of the image, which maintains the consistency between synthetic images and the corresponding labels, and further improves the reliability of the augmented data. Additionally, our method generates idiosyncratic shape-free mask for image, which helps the network learn more appropriate feature distributions from the diverse synthetic data. Finally, Experimental results demonstrate PlovMix significantly improves the generalization performance of popular deep networks on various datasets, such as CIFAR-10, CIFAR-100, Tiny ImageNet, and FGVC-Aircraft.