對抗機器學習——Min Max模型(Towards Deep Learning Models Resistant to Adversarial Attacks)
Towards Deep Learning Models Resistant to Adversarial Attacks 論文URL： https://arxiv.org/pdf/1706.06083.pdf 論文代碼： https://github.com/MadryLab/mnist_challenge 論文Key idea 本文提出了對抗機器學習領域裏面鼎鼎大名的Min-max最優化框架，
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- 3. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 4. 對抗機器學習基礎（論文學習筆記三）
- 5. [轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 6. 【對抗攻擊】【綜述】Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 7. 對抗機器學習：Generating Adversarial Malware Examples for Black-box Attacks Based on GAN
- 8. 對抗機器學習——Universal adversarial perturbations
- 9. 關於EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
- 10. 如何評估機器學習模型（How to Evaluate Machine Learning Models）
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相關文章
- 1. PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
- 2. 對抗機器學習：Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples
- 3. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 4. 對抗機器學習基礎（論文學習筆記三）
- 5. [轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 6. 【對抗攻擊】【綜述】Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 7. 對抗機器學習：Generating Adversarial Malware Examples for Black-box Attacks Based on GAN
- 8. 對抗機器學習——Universal adversarial perturbations
- 9. 關於EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples的理解
- 10. 如何評估機器學習模型（How to Evaluate Machine Learning Models）