Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lightweight Lipschitz Margin Training for Certified Defense against
  Adversarial Examples   How can we make machine learning provably robust against adversarial examples
in a scalable way? Since certified defense methods, which ensure
$\epsilon$-robust, consume huge resources, they can only achieve small degree
of robustness in practice. Lipschitz margin training (LMT) is a scalable
certified defense, but it can also only achieve small robustness due to
over-regularization. How can we make certified defense more efficiently? We
present LC-LMT, a light weight Lipschitz margin training which solves the above
problem. Our method has the following properties; (a) efficient: it can achieve
$\epsilon$-robustness at early epoch, and (b) robust: it has a potential to get
higher robustness than LMT. In the evaluation, we demonstrate the benefits of
the proposed method. LC-LMT can achieve required robustness more than 30 epoch
earlier than LMT in MNIST, and shows more than 90 $\%$ accuracy against both
legitimate and adversarial inputs.

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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-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