【Bias 02】BENCHMARKING NEURAL NETWORK ROBUSTNESS TO COMMON CORRUPTIONS AND PERTURBATIONS
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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相關文章
- 1. 【Bias 03】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
- 2. 關於Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks的理解
- 3. [paper]Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
- 4. 【數據增強】Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming
- 5. Adversarial Perturbations
- 6. 【GNN實驗新基準】Benchmarking Graph Neural Networks
- 7. [paper]Towards Evaluating the Robustness of Neural Networks(C&W)
- 8. Introduction to neural networks
- 9. 【譯】TensorFlow Tutorial #02 Convolutional Neural Network
- 10. Multi-Label Adversarial Perturbations