Improving model robustness in case of corrupted images is among the key
challenges to enable robust vision systems on smart devices, such as robotic
agents. Particularly, robust test-time performance is imperative for most of
the applications. This paper presents a novel approach to improve robustness of
any classification model, especially on severely corrupted images. Our method
(FROST) employs high-frequency features to detect input image corruption type,
and select layer-wise feature normalization statistics. FROST provides the
state-of-the-art results for different models and datasets, outperforming
competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of
40.9% mCE on severe corruptions.

为了在智能设备上实现强大的视觉系统，尤其是机器人代理，提高对损坏图像的模型稳健性是其中的关键挑战。本文介绍了一种新方法（FROST），通过使用高频特征检测输入图像的损坏类型，并选择逐层特征归一化统计，从而提高任何分类模型的稳健性，尤其是在严重损坏的图像上。FROST 方法在不同模型和数据集上取得了最新的研究成果，在 ImageNet-C 上相较于竞争对手的最大相对收益为 37.1%，改善了 40.9% 的严重损坏情况下的基线 mCE。