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.

采用傅里叶域检测损坏类型和更新 BatchNorm 统计信息的统一框架，可提高任何现成模型的损坏准确性，并进一步改善先进的稳健模型的准确性。

自然鲁棒性动态批归一化统计更新

Dynamic Batch Norm Statistics Update for Natural Robustness

Effective image restoration with large-size corruptions, such as blind image
inpainting, entails precise detection of corruption region masks which remains
extremely challenging due to diverse shapes and patterns of corruptions. In
this work, we present a novel method for automatic corruption detection, which
allows for blind corruption restoration without known corruption masks.
Specifically, we develop a hierarchical contrastive learning framework to
detect corrupted regions by capturing the intrinsic semantic distinctions
between corrupted and uncorrupted regions. In particular, our model detects the
corrupted mask in a coarse-to-fine manner by first predicting a coarse mask by
contrastive learning in low-resolution feature space and then refines the
uncertain area of the mask by high-resolution contrastive learning. A
specialized hierarchical interaction mechanism is designed to facilitate the
knowledge propagation of contrastive learning in different scales, boosting the
modeling performance substantially. The detected multi-scale corruption masks
are then leveraged to guide the corruption restoration. Detecting corrupted
regions by learning the contrastive distinctions rather than the semantic
patterns of corruptions, our model has well generalization ability across
different corruption patterns. Extensive experiments demonstrate following
merits of our model: 1) the superior performance over other methods on both
corruption detection and various image restoration tasks including blind
inpainting and watermark removal, and 2) strong generalization across different
corruption patterns such as graffiti, random noise or other image content.
Codes and trained weights are available at this https URL .

研究论文提出了一种通过对比学习来检测图像中大尺寸污损区域的方法，并且克服了不同污损模式的挑战，实现了图像修复的优越性能和泛化能力。