Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Architectural Ingredients of Adversarially Robust Deep Neural
  Networks   Deep neural networks (DNNs) are known to be vulnerable to adversarial
attacks. A range of defense methods have been proposed to train adversarially
robust DNNs, among which adversarial training has demonstrated promising
results. However, despite preliminary understandings developed for adversarial
training, it is still not clear, from the architectural perspective, what
configurations can lead to more robust DNNs. In this paper, we address this gap
via a comprehensive investigation on the impact of network width and depth on
the robustness of adversarially trained DNNs. Specifically, we make the
following key observations: 1) more parameters (higher model capacity) does not
necessarily help adversarial robustness; 2) reducing capacity at the last stage
(the last group of blocks) of the network can actually improve adversarial
robustness; and 3) under the same parameter budget, there exists an optimal
architectural configuration for adversarial robustness. We also provide a
theoretical analysis explaning why such network configuration can help
robustness. These architectural insights can help design adversarially robust
DNNs. Code is available at \url{https://github.com/HanxunH/RobustWRN}.

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The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: math-ph