Stochastic Batch Augmentation with An Effective Distilled Dynamic Soft Label Regularizer

TL;DR

This paper introduces Stochastic Batch Augmentation (SBA), a novel training framework that enhances neural network generalization by incorporating distribution-aware soft label regularization during data augmentation.

Contribution

The work proposes SBA, which stochastically applies augmentation with a dynamic soft label regularizer based on distribution similarity, improving training efficiency and model generalization.

Findings

SBA improves generalization on CIFAR-10, CIFAR-100, and ImageNet.

SBA accelerates convergence during training.

SBA outperforms traditional augmentation methods.

Abstract

Data augmentation have been intensively used in training deep neural network to improve the generalization, whether in original space (e.g., image space) or representation space. Although being successful, the connection between the synthesized data and the original data is largely ignored in training, without considering the distribution information that the synthesized samples are surrounding the original sample in training. Hence, the behavior of the network is not optimized for this. However, that behavior is crucially important for generalization, even in the adversarial setting, for the safety of the deep learning system. In this work, we propose a framework called Stochastic Batch Augmentation (SBA) to address these problems. SBA stochastically decides whether to augment at iterations controlled by the batch scheduler and in which a ''distilled'' dynamic soft label regularization is introduced by incorporating the similarity in the vicinity distribution respect to raw samples. The proposed regularization provides direct supervision by the KL-Divergence between the output soft-max distributions of original and virtual data. Our experiments on CIFAR-10, CIFAR-100, and ImageNet show that SBA can improve the generalization of the neural networks and speed up the convergence of network training.