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Competition on robust deep learning
This perspective paper proposes a new adversarial training method based on large-scale pre-trained models to achieve state-of-the-art adversarial robustness on ImageNet.
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257479/ https://www.ncbi.nlm.nih.gov/pubmed/37304460 http://dx.doi.org/10.1093/nsr/nwad087 |
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author | Dong, Yinpeng Liu, Chang Xiang, Wenzhao Su, Hang Zhu, Jun |
author_facet | Dong, Yinpeng Liu, Chang Xiang, Wenzhao Su, Hang Zhu, Jun |
author_sort | Dong, Yinpeng |
collection | PubMed |
description | This perspective paper proposes a new adversarial training method based on large-scale pre-trained models to achieve state-of-the-art adversarial robustness on ImageNet. |
format | Online Article Text |
id | pubmed-10257479 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-102574792023-06-11 Competition on robust deep learning Dong, Yinpeng Liu, Chang Xiang, Wenzhao Su, Hang Zhu, Jun Natl Sci Rev PERSPECTIVE This perspective paper proposes a new adversarial training method based on large-scale pre-trained models to achieve state-of-the-art adversarial robustness on ImageNet. Oxford University Press 2023-04-07 /pmc/articles/PMC10257479/ /pubmed/37304460 http://dx.doi.org/10.1093/nsr/nwad087 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | PERSPECTIVE Dong, Yinpeng Liu, Chang Xiang, Wenzhao Su, Hang Zhu, Jun Competition on robust deep learning |
title | Competition on robust deep learning |
title_full | Competition on robust deep learning |
title_fullStr | Competition on robust deep learning |
title_full_unstemmed | Competition on robust deep learning |
title_short | Competition on robust deep learning |
title_sort | competition on robust deep learning |
topic | PERSPECTIVE |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257479/ https://www.ncbi.nlm.nih.gov/pubmed/37304460 http://dx.doi.org/10.1093/nsr/nwad087 |
work_keys_str_mv | AT dongyinpeng competitiononrobustdeeplearning AT liuchang competitiononrobustdeeplearning AT xiangwenzhao competitiononrobustdeeplearning AT suhang competitiononrobustdeeplearning AT zhujun competitiononrobustdeeplearning |