Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Robustness via Label-Smoothing   We study Label-Smoothing as a means for improving adversarial robustness of
supervised deep-learning models. After establishing a thorough and unified
framework, we propose several variations to this general method: adversarial,
Boltzmann and second-best Label-Smoothing methods, and we explain how to
construct your own one. On various datasets (MNIST, CIFAR10, SVHN) and models
(linear models, MLPs, LeNet, ResNet), we show that Label-Smoothing in general
improves adversarial robustness against a variety of attacks (FGSM, BIM,
DeepFool, Carlini-Wagner) by better taking account of the dataset geometry. The
proposed Label-Smoothing methods have two main advantages: they can be
implemented as a modified cross-entropy loss, thus do not require any
modifications of the network architecture nor do they lead to increased
training times, and they improve both standard and adversarial accuracy.

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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th