對抗樣本論文學習：Deep Neural Networks are Easily Fooled
近日看了一些對抗樣本（adversarial examples）方面的論文，在這裏對這些論文進行一下整理和總結。 以下僅代表個人理解，本人能力有限難免有錯，還請大家給予糾正，不勝感激。歡迎一起討論進步。 對抗樣本（adversarial examples）和對抗學習（GAN）截然不同，這一概念在Szegedy et al. (2014b)中提出：對輸入樣本故意添加一些人無法察覺的細微的干擾，導致模
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相關文章
- 1. [論文筆記]Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
- 2. [deep compression] compressing deep neural networks 論文學習
- 3. 【生成對抗樣本】Simple Black-Box Adversarial Attacks on Deep Neural Networks
- 4. Adversarial Examples 對抗樣本
- 5. Neural Networks and Deep Learning-引論
- 6. How transferable are features in deep neural networks? 論文筆記
- 7. 論文筆記：How transferable are features in deep neural networks?
- 8. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 9. 論文學習：Deep Convolutional Neural Networks On Multichannel Time Series For Human Activity Recognition
- 10. 【深度學習NLP論文筆記】《Deep Text Classification Can be Fooled》