Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Invariant Adversarial Learning   The phenomenon of adversarial examples illustrates one of the most basic
vulnerabilities of deep neural networks. Among the variety of techniques
introduced to surmount this inherent weakness, adversarial training has emerged
as the most effective strategy to achieve robustness. Typically, this is
achieved by balancing robust and natural objectives. In this work, we aim to
further optimize the trade-off between robust and standard accuracy by
enforcing a domain-invariant feature representation. We present a new
adversarial training method, Domain Invariant Adversarial Learning (DIAL),
which learns a feature representation that is both robust and domain invariant.
DIAL uses a variant of Domain Adversarial Neural Network (DANN) on the natural
domain and its corresponding adversarial domain. In the case where the source
domain consists of natural examples and the target domain is the adversarially
perturbed examples, our method learns a feature representation constrained not
to discriminate between the natural and adversarial examples, and can therefore
achieve a more robust representation. Our experiments indicate that our method
improves both robustness and standard accuracy, when compared to other
state-of-the-art adversarial training methods.

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The categories of this paper are: astro-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: cond-mat
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The categories of this paper are: dg-ga
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin