  Out-of-distribution generalization can be categorized into two types: common
perturbations arising from natural variations in the real world and adversarial
perturbations that are intentionally crafted to deceive neural networks. While
deep neural networks excel in accuracy under the assumption of identical
distributions between training and test data, they often encounter
out-of-distribution scenarios resulting in a significant decline in accuracy.
Data augmentation methods can effectively enhance robustness against common
corruptions, but they typically fall short in improving robustness against
adversarial perturbations. In this study, we develop Label Augmentation (LA),
which enhances robustness against both common and intentional perturbations and
improves uncertainty estimation. Our findings indicate a Clean error rate
improvement of up to 23.29% when employing LA in comparisons to the baseline.
Additionally, it enhances robustness under common corruptions benchmark by up
to 24.23%. When tested against FGSM and PGD attacks, improvements in
adversarial robustness are noticeable, with enhancements of up to 53.18% for
FGSM and 24.46% for PGD attacks.
