[paper]Towards Evaluating the Robustness of Neural Networks(C&W)
本文提出了針對Denfensive distillation這種防禦措施的C&W算法（基於三種不同距離的對抗樣本生成算法），同時也具有一定的遷移性。 本文把構建對抗樣本的過程轉化爲一個最優化問題： 其中 D D D是衡量原始圖像與對抗樣本之間的距離, 三種不同距離分別爲 L 0 L_{0} L0範數 L 2 L_{2} L2範數和 L ∞ L_{\infty } L∞範數。 但由於 C (
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相關文章
- 1. Towards Evaluating the Robustness of Neural Networks論文解讀集合
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- 3. Explain the two methods of evaluating relational expressions
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- 5. On the Number of Linear Regions of Deep Neural Networks
- 6. KNN魯棒性訓練之On the Robustness of Deep K-Nearest Neighbors
- 7. [解讀] Improving the Robustness of Capsule Networks to Image Affine Transformations
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- 9. Xavier——Understanding the difficulty of training deep feedforward neural networks
- 10. On the difficulty of training Recurrent Neural Networks