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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 classi...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2456035554

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

About this item

Full title

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2020-10

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

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...

Alternative Titles

Full title

Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2456035554

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2456035554

Other Identifiers

E-ISSN

2331-8422

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