Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Robustness of Convolutional Neural Networks Using Element-Wise
  Activation Scaling   Recent works reveal that re-calibrating the intermediate activation of
adversarial examples can improve the adversarial robustness of a CNN model. The
state of the arts [Baiet al., 2021] and [Yanet al., 2021] explores this feature
at the channel level, i.e. the activation of a channel is uniformly scaled by a
factor. In this paper, we investigate the intermediate activation manipulation
at a more fine-grained level. Instead of uniformly scaling the activation, we
individually adjust each element within an activation and thus propose
Element-Wise Activation Scaling, dubbed EWAS, to improve CNNs' adversarial
robustness. Experimental results on ResNet-18 and WideResNet with CIFAR10 and
SVHN show that EWAS significantly improves the robustness accuracy. Especially
for ResNet18 on CIFAR10, EWAS increases the adversarial accuracy by 37.65% to
82.35% against C&W attack. EWAS is simple yet very effective in terms of
improving robustness. The codes are anonymously available at
https://anonymous.4open.science/r/EWAS-DD64.

21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int