Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Regularized Training and Tight Certification for Randomized Smoothed
  Classifier with Provable Robustness   Recently smoothing deep neural network based classifiers via isotropic
Gaussian perturbation is shown to be an effective and scalable way to provide
state-of-the-art probabilistic robustness guarantee against $\ell_2$ norm
bounded adversarial perturbations. However, how to train a good base classifier
that is accurate and robust when smoothed has not been fully investigated. In
this work, we derive a new regularized risk, in which the regularizer can
adaptively encourage the accuracy and robustness of the smoothed counterpart
when training the base classifier. It is computationally efficient and can be
implemented in parallel with other empirical defense methods. We discuss how to
implement it under both standard (non-adversarial) and adversarial training
scheme. At the same time, we also design a new certification algorithm, which
can leverage the regularization effect to provide tighter robustness lower
bound that holds with high probability. Our extensive experimentation
demonstrates the effectiveness of the proposed training and certification
approaches on CIFAR-10 and ImageNet datasets.

32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: funct-an
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The categories of this paper are: astro-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: plasm-ph
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The categories of this paper are: q-alg