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關於The Limitations of Deep Learning in Adversarial Settings的理解
時間 2020-12-29
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與之前的基於提高原始類別標記的損失函數或者降低目標類別標記的損失函數的方式不同,這篇文章提出直接增加神經網絡對目標類別的預測值。換句話說,之前的對抗樣本的擾動方向都是損失函數的梯度方向(無論是原始類別標記的損失函數還是目標類別標記的損失函數),該論文生成的對抗樣本的擾動方向是目標類別標記的預測值的梯度方向,作者將這個梯度稱爲前向梯度(forward derivative)。即: ∇F(X)=∂F
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相關文章
1.
[paper]The Limitations of Deep Learning in Adversarial Settings(JSMA)
2.
The Limitations of Deep Learning in Adversarial Settings
3.
對The Limitations of Deep Learning in Adversarial Settings理解
4.
【論文回顧】The Limitations of Deep Learning in Adversarial Settings
5.
The Limitations of Deep Learning in Adversarial Settings論文筆記
6.
Exploring the teaching of deep learning in neural networks
7.
[論文解讀]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
8.
Application of deep learning in Industrial area
9.
The Rise of Meta Learning
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[轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
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