Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classi...
Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in proble...
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Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification
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TN_cdi_proquest_journals_2456035554
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2456035554
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2331-8422