Non-adversarial Robustness of Deep Learning Methods for Computer Vision
Gorana Gojić Vladimir Vincan Ognjen Kundačina Dragiša Mišković Dinu Dragan
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.