JavaShuo
欄目
標籤
關於The Limitations of Deep Learning in Adversarial Settings的理解
時間 2020-12-29
標籤
對抗樣本
简体版
原文
原文鏈接
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
>>阅读原文<<
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
10.
[轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
更多相關文章...
•
MyBatis settings
-
MyBatis教程
•
Swift for-in 循環
-
Swift 教程
•
☆基於Java Instrument的Agent實現
•
NewSQL-TiDB相關
相關標籤/搜索
Deep Learning
adversarial
settings
limitations
learning
deep
關於
我的理解
理解
for...of
NoSQL教程
MySQL教程
Spring教程
0
分享到微博
分享到微信
分享到QQ
每日一句
每一个你不满意的现在,都有一个你没有努力的曾经。
最新文章
1.
1.2 Illustrator多文檔的幾種排列方式
2.
5.16--java數據類型轉換及雜記
3.
性能指標
4.
(1.2)工廠模式之工廠方法模式
5.
Java記錄 -42- Java Collection
6.
Java記錄 -42- Java Collection
7.
github使用
8.
Android學習筆記(五十):聲明、請求和檢查許可
9.
20180626
10.
服務擴容可能引入的負面問題及解決方法
本站公眾號
歡迎關注本站公眾號,獲取更多信息
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
10.
[轉載][paper]Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
>>更多相關文章<<