  Deep neural networks have become popular in many supervised learning tasks,
but they may suffer from overfitting when the training dataset is limited. To
mitigate this, many researchers use data augmentation, which is a widely used
and effective method for increasing the variety of datasets. However, the
randomness introduced by data augmentation causes inevitable inconsistency
between training and inference, which leads to poor improvement. In this paper,
we propose a consistency regularization framework based on data augmentation,
called CR-Aug, which forces the output distributions of different sub models
generated by data augmentation to be consistent with each other. Specifically,
CR-Aug evaluates the discrepancy between the output distributions of two
augmented versions of each sample, and it utilizes a stop-gradient operation to
minimize the consistency loss. We implement CR-Aug to image and audio
classification tasks and conduct extensive experiments to verify its
effectiveness in improving the generalization ability of classifiers. Our
CR-Aug framework is ready-to-use, it can be easily adapted to many
state-of-the-art network architectures. Our empirical results show that CR-Aug
outperforms baseline methods by a significant margin.
