DNNs trained on natural clean samples have been shown to perform poorly on corrupted samples, such as noisy or blurry images. Various data augmentation methods have been recently proposed to improve DNN's robustness against common corruptions. Despite their success, they require computationally expensive training and cannot be applied to off-the-shelf trained models. Recently, it has been shown that updating BatchNorm (BN) statistics of an off-the-shelf model on a single corruption improves its accuracy on that corruption significantly. However, adopting the idea at inference time when the type of corruption is unknown and changing decreases the effectiveness of this method. In this paper, we harness the Fourier domain to detect the corruption type, a challenging task in the image domain. We propose a unified framework consisting of a corruption-detection model and BN statistics update that improves the corruption accuracy of any off-the-shelf trained model. We benchmark our framework on different models and datasets. Our results demonstrate about 8% and 4% accuracy improvement on CIFAR10-C and ImageNet-C, respectively. Furthermore, our framework can further improve the accuracy of state-of-the-art robust models, such as AugMix and DeepAug.

翻译：深度神经网络在自然干净样本上训练后，在含噪或模糊等污染样本上表现不佳。近年来，多种数据增强方法被提出用于提升深度神经网络对常见污染的鲁棒性。尽管这些方法取得成效，但需要高计算成本的训练过程，且无法直接应用于预训练模型。最近研究发现，针对单一污染类型更新预训练模型的批归一化（BatchNorm）统计量，可显著提升模型在该污染上的准确率。然而，在推理阶段面对未知且动态变化的污染类型时，该方法的有效性会下降。本文利用傅里叶域检测污染类型（这在图像域中极具挑战性），提出一个包含污染检测模型与批归一化统计更新的统一框架，可提升任意预训练模型对污染数据的准确率。我们在不同模型与数据集上验证该框架的性能。结果表明，在CIFAR10-C与ImageNet-C上分别实现约8%和4%的准确率提升。此外，该框架能进一步改进如AugMix和DeepAug等先进鲁棒模型的准确率。