[paper]IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES
對抗訓練是當前最有效的防禦措施，經常被公式化爲最小-最大優化問題。 where n is the number of training examples and l l l(·) is the classification loss, such as the commonly used cross-entropy (CE) loss.Recently, adversarial training wi
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相關文章
- 1. Certified Robustness to Adversarial Examples with Differential Privacy
- 2. ROBUSTNESS TO ADVERSARIAL EXAMPLES THROUGH AN ENSEMBLE OF SPECIALISTS
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- 4. Paper Review: Adversarial Examples
- 5. [advGAN]Generating Adversarial Examples With Adversarial Networks
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- 10. CS231n Lecture 16 | Adversarial Examples and Adversarial Training