  The performance of computer vision models are susceptible to unexpected
changes in input images, known as common corruptions (e.g. noise, blur,
illumination changes, etc.), that can hinder their reliability when deployed in
real scenarios. These corruptions are not always considered to test model
generalization and robustness. In this survey, we present a comprehensive
overview of methods that improve the robustness of computer vision models
against common corruptions. We categorize methods into four groups based on the
model part and training method addressed: data augmentation, representation
learning, knowledge distillation, and network components. We also cover
indirect methods for generalization and mitigation of shortcut learning,
potentially useful for corruption robustness. We release a unified benchmark
framework to compare robustness performance on several datasets, and address
the inconsistencies of evaluation in the literature. We provide an experimental
overview of the base corruption robustness of popular vision backbones, and
show that corruption robustness does not necessarily scale with model size. The
very large models (above 100M parameters) gain negligible robustness,
considering the increased computational requirements. To achieve generalizable
and robust computer vision models, we foresee the need of developing new
learning strategies to efficiently exploit limited data and mitigate unwanted
or unreliable learning behaviors.
