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.

本研究解决了神经网络在常见扰动和对抗扰动中的鲁棒性不足问题。提出的标签增强（LA）方法旨在同时提高对这两种扰动的鲁棒性，并改善不确定性估计。研究结果显示，与基线相比，LA在清洁错误率上提高了最多23.29%，并在常见扰动基准上提高了最多24.23%的鲁棒性。 

神经网络鲁棒性的标签增强