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Then we propose a new dataset called ImageNet-P\nwhich enables researchers to benchmark a classifier's robustness to common\nperturbations. Unlike recent robustness research, this benchmark evaluates\nperformance on common corruptions and perturbations not worst-case adversarial\nperturbations. We find that there are negligible changes in relative corruption\nrobustness from AlexNet classifiers to ResNet classifiers. Afterward we\ndiscover ways to enhance corruption and perturbation robustness. We even find\nthat a bypassed adversarial defense provides substantial common perturbation\nrobustness. Together our benchmarks may aid future work toward networks that\nrobustly generalize.","url_abs":"http://arxiv.org/abs/1903.12261v1","url_pdf":"http://arxiv.org/pdf/1903.12261v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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