  With the increasing utilization of deep learning in outdoor settings, its
robustness needs to be enhanced to preserve accuracy in the face of
distribution shifts, such as compression artifacts. Data augmentation is a
widely used technique to improve robustness, thanks to its ease of use and
numerous benefits. However, it requires more training epochs, making it
difficult to train large models with limited computational resources. To
address this problem, we treat data augmentation as supervised domain
generalization~(SDG) and benefit from the SDG method, contrastive semantic
alignment~(CSA) loss, to improve the robustness and training efficiency of data
augmentation. The proposed method only adds loss during model training and can
be used as a plug-in for existing data augmentation methods. Experiments on the
CIFAR-100 and CUB datasets show that the proposed method improves the
robustness and training efficiency of typical data augmentations.
