Data augmentation has been proven effective for training high-accuracy convolutional neural network classifiers by preventing overfitting. However, building deep neural networks in real-world scenarios requires not only high accuracy on clean data but also robustness when data distributions shift. While prior methods have proposed that there is a trade-off between accuracy and robustness, we propose IPMix, a simple data augmentation approach to improve robustness without hurting clean accuracy. IPMix integrates three levels of data augmentation (image-level, patch-level, and pixel-level) into a coherent and label-preserving technique to increase the diversity of training data with limited computational overhead. To further improve the robustness, IPMix introduces structural complexity at different levels to generate more diverse images and adopts the random mixing method for multi-scale information fusion. Experiments demonstrate that IPMix outperforms state-of-the-art corruption robustness on CIFAR-C and ImageNet-C. In addition, we show that IPMix also significantly improves the other safety measures, including robustness to adversarial perturbations, calibration, prediction consistency, and anomaly detection, achieving state-of-the-art or comparable results on several benchmarks, including ImageNet-R, ImageNet-A, and ImageNet-O.

翻译：数据增强通过防止过拟合已被证明对训练高精度卷积神经网络分类器有效。然而，在真实场景中构建深度神经网络不仅需要在干净数据上达到高精度，还需要在数据分布发生偏移时具备鲁棒性。尽管先前方法提出精度与鲁棒性之间存在权衡，我们提出了IPMix——一种简单数据增强方法，可在不降低干净数据精度的前提下提升鲁棒性。IPMix将三个层面（图像级、补丁级和像素级）的数据增强整合为连贯且保持标签的技术，以有限计算开销增加训练数据多样性。为进一步提升鲁棒性，IPMix在不同层面引入结构复杂性以生成更多样化图像，并采用随机混合方法实现多尺度信息融合。实验表明，IPMix在CIFAR-C和ImageNet-C上优于最先进的腐蚀鲁棒性方法。此外，我们证明IPMix还显著改善了其他安全指标，包括对对抗扰动的鲁棒性、校准性能、预测一致性及异常检测，在ImageNet-R、ImageNet-A和ImageNet-O等多个基准测试中达到最优或可比结果。