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ImageNet-C/P

ImageNet with Corruptions and Perturbations for Benchmarking Network Robustness

ImageNet-C/P

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. Then we propose a new dataset called ImageNet-P which enables researchers to benchmark a classifier's robustness to common perturbations. Unlike recent robustness research, this benchmark evaluates performance on common corruptions and perturbations not worst-case adversarial perturbations. We find that there are negligible changes in relative corruption robustness from AlexNet classifiers to ResNet classifiers. Afterward we discover ways to enhance corruption and perturbation robustness. We even find that a bypassed adversarial defense provides substantial common perturbation robustness. Together our benchmarks may aid future work toward networks that robustly generalize.

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Task
Image Classification
Annotation Types
Classification Tags
200
Items
1000
Classes
200
Labels
Models using this dataset
Last updated on 
January 20, 2022
Licensed under 
MIT
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