發(fā)簡信
IP屬地：廣東
-
Adversarial Distributional Training
Adversarial Distributional Training for Robust Deep LearningZhijie Deng,...
-
定制化對抗訓練Customized Adversarial TrainingCAT: Customized Adversarial Training for Improved RobustnessMinhao Cheng...
-
聚類訓練Clustering Training for Robustness
ClustTR: Clustering Training for RobustnessMotasem Alfarra, Juan C. Pére...
-
對抗樣本領域綜述文章推薦
對于剛接觸對抗樣本領域的小伙伴們來說，看到領域內(nèi)眾多文章時，簡直眼花繚亂。這時候，如果一篇好的綜述概括了當前領域內(nèi)的主要進展，提供給我們該領域的...
-
[論文筆記]DeepFool: a simple and accurate method to fool deep neural networks題目：DeepFool: a simple and accurate method to fool deep neural networks地址...
-
[論文筆記]Towards Evaluating the Robustness of Neural Networks
題目：Towards Evaluating the Robustness of Neural Networks地址：https://arxiv....
-
[論文閱讀筆記]One pixel attack for fooling deep neural networks
論文題目：One pixel attack for fooling deep neural networks論文地址：https://arxiv...
-
對抗攻擊方法匯總（持續(xù)更新）
自從2014年Szegedy等人提出對抗樣本以來，不斷有研究者提出新的對抗攻擊方法。本文匯總了當前已有的絕大多數(shù)算法，以拋磚引玉用，并不斷更新。...
-
[論文閱讀筆記]The Limitations of Deep Learning in Adversarial Settings論文題目：The Limitations of Deep Learning in Adversarial Settings論文地址：https:...