Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make
  Student Better   Adversarial training is one effective approach for training robust deep
neural networks against adversarial attacks. While being able to bring reliable
robustness, adversarial training (AT) methods in general favor high capacity
models, i.e., the larger the model the better the robustness. This tends to
limit their effectiveness on small models, which are more preferable in
scenarios where storage or computing resources are very limited (e.g., mobile
devices). In this paper, we leverage the concept of knowledge distillation to
improve the robustness of small models by distilling from adversarially trained
large models. We first revisit several state-of-the-art AT methods from a
distillation perspective and identify one common technique that can lead to
improved robustness: the use of robust soft labels -- predictions of a robust
model. Following this observation, we propose a novel adversarial robustness
distillation method called Robust Soft Label Adversarial Distillation (RSLAD)
to train robust small student models. RSLAD fully exploits the robust soft
labels produced by a robust (adversarially-trained) large teacher model to
guide the student's learning on both natural and adversarial examples in all
loss terms. We empirically demonstrate the effectiveness of our RSLAD approach
over existing adversarial training and distillation methods in improving the
robustness of small models against state-of-the-art attacks including the
AutoAttack. We also provide a set of understandings on our RSLAD and the
importance of robust soft labels for adversarial robustness distillation.

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The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio