Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pruning in the Face of Adversaries   The vulnerability of deep neural networks against adversarial examples -
inputs with small imperceptible perturbations - has gained a lot of attention
in the research community recently. Simultaneously, the number of parameters of
state-of-the-art deep learning models has been growing massively, with
implications on the memory and computational resources required to train and
deploy such models. One approach to control the size of neural networks is
retrospectively reducing the number of parameters, so-called neural network
pruning. Available research on the impact of neural network pruning on the
adversarial robustness is fragmentary and often does not adhere to established
principles of robustness evaluation. We close this gap by evaluating the
robustness of pruned models against L-0, L-2 and L-infinity attacks for a wide
range of attack strengths, several architectures, data sets, pruning methods,
and compression rates. Our results confirm that neural network pruning and
adversarial robustness are not mutually exclusive. Instead, sweet spots can be
found that are favorable in terms of model size and adversarial robustness.
Furthermore, we extend our analysis to situations that incorporate additional
assumptions on the adversarial scenario and show that depending on the
situation, different strategies are optimal.

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The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
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The categories of this paper are: alg-geom
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph