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ROBUSTNESS TO ADVERSARIAL EXAMPLES THROUGH AN ENSEMBLE OF SPECIALISTS
时间 2020-12-30
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1.利用FGSM方法得到模型的混淆矩阵: 根据混淆矩阵将数据分为2K+1个子集,在每个子集上训练分类器。 2.伪代码如下: 2k个专家子集和一个训练10分类的cnn ensemble 3.实验考虑三种模型 单个CNN,简单集成CNN,specialists +1,其中每个卷积网都是(32+32+64+全连接+softmax。 投票机制无法使用通才来激活相关专家,因为通常这会遭到对手的欺骗。在算法1
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相关文章
1.
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
2.
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3.
[paper]IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES
4.
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