【Bias 02】BENCHMARKING NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS AND PERTURBATIONS
摘要 对图像分类的鲁棒性评估，建立benchmark。我们的第一个benchmark是ImageNet-C，它可以评估哪一个分类器更适合安全关键的应用。第二个benchmark是ImageNet-P，使得研究人员可以衡量分类器对常见扰动的鲁棒性。并且本文探究加强腐蚀和扰动的鲁棒性，本文甚至发现bypassed adversarial defense提供了对于常见扰动的鲁棒性。 Related
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相关文章
- 1. 【Bias 03】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
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- 3. [paper]Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- 4. 【数据增强】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
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