Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GradDiv: Adversarial Robustness of Randomized Neural Networks via
  Gradient Diversity Regularization   Deep learning is vulnerable to adversarial examples. Many defenses based on
randomized neural networks have been proposed to solve the problem, but fail to
achieve robustness against attacks using proxy gradients such as the
Expectation over Transformation (EOT) attack. We investigate the effect of the
adversarial attacks using proxy gradients on randomized neural networks and
demonstrate that it highly relies on the directional distribution of the loss
gradients of the randomized neural network. We show in particular that proxy
gradients are less effective when the gradients are more scattered. To this
end, we propose Gradient Diversity (GradDiv) regularizations that minimize the
concentration of the gradients to build a robust randomized neural network. Our
experiments on MNIST, CIFAR10, and STL10 show that our proposed GradDiv
regularizations improve the adversarial robustness of randomized neural
networks against a variety of state-of-the-art attack methods. Moreover, our
method efficiently reduces the transferability among sample models of
randomized neural networks.

9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: adap-org
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The categories of this paper are: eess
30
The categories of this paper are: q-alg