Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training
Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training
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Cham: Springer International Publishing
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English
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Cham: Springer International Publishing
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In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes o...
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Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training
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TN_cdi_doaj_primary_oai_doaj_org_article_b0ad3f4b7c7946e8b71a6fcc9cca422f
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_b0ad3f4b7c7946e8b71a6fcc9cca422f
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ISSN
2510-523X,1687-4161
E-ISSN
2510-523X,1687-417X
DOI
10.1186/s13635-023-00137-0
