Non-adversarial robustness, also known as natural robustness, is a property
of deep learning models that enables them to maintain performance even when
faced with distribution shifts caused by natural variations in data. However,
achieving this property is challenging because it is difficult to predict in
advance the types of distribution shifts that may occur. To address this
challenge, researchers have proposed various approaches, some of which
anticipate potential distribution shifts, while others utilize knowledge about
the shifts that have already occurred to enhance model generalizability. In
this paper, we present a brief overview of the most recent techniques for
improving the robustness of computer vision methods, as well as a summary of
commonly used robustness benchmark datasets for evaluating the model's
performance under data distribution shifts. Finally, we examine the strengths
and limitations of the approaches reviewed and identify general trends in deep
learning robustness improvement for computer vision.

本论文简要概述了提高计算机视觉模型稳健性的最新技术，以及用于评估模型性能的常用鲁棒性基准数据集，并审视了所审查方法的优点和局限性，鉴别了深度学习稳健性改进的一般趋势。