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Fast Adversarial Training with Dynamic Batch-level Attack Control

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dc.contributor.authorJung, Jaewon-
dc.contributor.authorSong, Jaeyong-
dc.contributor.authorJang, Hongsun-
dc.contributor.authorLee, Hyeyoon-
dc.contributor.authorChoi, Kanghyun-
dc.contributor.authorPark, Noseong-
dc.contributor.authorLee, Jinho-
dc.date.accessioned2023-11-15T04:29:48Z-
dc.date.available2023-11-15T04:29:48Z-
dc.date.created2023-08-21-
dc.date.created2023-08-21-
dc.date.created2023-08-21-
dc.date.issued2023-07-
dc.identifier.citationProceedings - Design Automation Conference-
dc.identifier.issn0146-7123-
dc.identifier.urihttps://hdl.handle.net/10371/196106-
dc.language영어-
dc.publisherProceedings - Design Automation Conference-
dc.titleFast Adversarial Training with Dynamic Batch-level Attack Control-
dc.typeArticle-
dc.citation.journaltitleProceedings - Design Automation Conference-
dc.identifier.wosid001073487300237-
dc.identifier.scopusid2-s2.0-85173098575-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorLee, Jinho-
dc.type.docType원저(Article)-
dc.description.journalClass1-
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  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area AI Accelerators, Distributed Deep Learning, Neural Architecture Search

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