【Bias 02】BENCHMARKING NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS AND PERTURBATIONS

時間 2021-06-11

標籤 Bias 欄目系統網絡简体版

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摘要對圖像分類的魯棒性評估，建立benchmark。我們的第一個benchmark是ImageNet-C，它可以評估哪一個分類器更適合安全關鍵的應用。第二個benchmark是ImageNet-P，使得研究人員可以衡量分類器對常見擾動的魯棒性。並且本文探究加強腐蝕和擾動的魯棒性，本文甚至發現bypassed adversarial defense提供了對於常見擾動的魯棒性。 Related

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