Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations

TL;DR

This paper introduces ImageNet-C, a benchmark for evaluating image classifier robustness to common corruptions, and Icons-50, a dataset for surface variation robustness, along with methods to improve these robustness aspects.

Contribution

It establishes standardized benchmarks for corruption and surface variation robustness, and proposes methods to enhance robustness in neural networks.

Findings

Negligible change in corruption robustness from AlexNet to ResNet

Methods to improve corruption robustness

Evaluation of classifiers on surface variation robustness

Abstract

In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize.