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對The Limitations of Deep Learning in Adversarial Settings理解
時間 2021-07-11
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The Limitations of Deep Learning in Adversarial Settings理解 對抗設置中深度學習的侷限性 摘要 - 深度學習利用大型數據集和計算上有效的訓練算法,在各種機器學習任務中勝過其他方法。然而,深度神經網絡訓練階段的不完善使得它們容易受到對抗樣本的攻擊:由對手製造的輸入旨在導致深度神經網絡錯誤分類。在這項工作中,我們將對手的空間形式化爲深度神經網絡(
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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.
關於The Limitations of Deep Learning in Adversarial Settings的理解
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.
The Rise of Meta Learning
9.
Application of deep learning in Industrial area
10.
[轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
>>更多相關文章<<