Today's state-of-the-art machine vision models are vulnerable to image
corruptions like blurring or compression artefacts, limiting their performance
in many real-world applications. We here argue that popular benchmarks to
measure model robustness against common corruptions (like ImageNet-C)
underestimate model robustness in many (but not all) application scenarios. The
key insight is that in many scenarios, multiple unlabeled examples of the
corruptions are available and can be used for unsupervised online adaptation.
Replacing the activation statistics estimated by batch normalization on the
training set with the statistics of the corrupted images consistently improves
the robustness across 25 different popular computer vision models. Using the
corrected statistics, ResNet-50 reaches 62.2% mCE on ImageNet-C compared to
76.7% without adaptation. With the more robust DeepAugment+AugMix model, we
improve the state of the art achieved by a ResNet50 model up to date from 53.6%
mCE to 45.4% mCE. Even adapting to a single sample improves robustness for the
ResNet-50 and AugMix models, and 32 samples are sufficient to improve the
current state of the art for a ResNet-50 architecture. We argue that results
with adapted statistics should be included whenever reporting scores in
corruption benchmarks and other out-of-distribution generalization settings.

本研究证明：大多数图像较差的基准测试不能很好地衡量图像识别模型在许多场景下的稳健性，因此提出了一种基于非监督在线适应的方法，通过更改模型激活的统计特征来提高模型的稳健性。改进后的模型可在 ImageNet-C 数据集上取得更好的表现。