Benchmarking Neural Network Robustness to Common Corruptions and Perturbations

ICLR 2019 Dan HendrycksThomas Dietterich

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... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Domain Generalization ImageNet-C ResNet-50 mean Corruption Error (mCE) 76.7 # 4

Methods used in the Paper