Model-Reuse Attacks on Deep Learning Systems
Model-Reuse Attacks on Deep Learning Systems閱讀筆記 文獻背景及解決問題 核心方法 論文優點 總結與想法 文獻背景及解決問題 現如今許多機器學習(ML)系統都是通過重用一組通常經過預先訓練的原始模型來構建的(例如，特徵提取)，越來越多的原始模型的使用意味着簡化和加速了ML系統的開發週期，但由於大多數此類模型都是由不可信的來源提供和維護的，缺乏標準化或監管
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- 1. Model-Reuse Attacks on Deep Learning Systems
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- 3. Paper Reading:Wide & Deep Learning for Recommender Systems
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- 7. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 8. [轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 9. PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
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相關文章
- 1. Model-Reuse Attacks on Deep Learning Systems
- 2. A Survey of Recommender Systems Based on Deep Learning （1）
- 3. Paper Reading:Wide & Deep Learning for Recommender Systems
- 4. Wide & Deep Learning for Recommender Systems
- 5. Collaborative Deep Learning for Recommender Systems
- 6. Wide &Deep Learning for Recommender Systems
- 7. [論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 8. [轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
- 9. PGD:Towards Deep Learning Models Resistant to Adversarial Attacks
- 10. 「Deep Learning」Note on RetinaNet