Rethinking data augmentation for adversarial robustness-Reference-Cited by-同舟云学术

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Rethinking data augmentation for adversarial robustness

Published:2024-01 Issue: Volume:654 Page:119838
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Eghbal-zadeh Hamid^ORCID,Zellinger Werner^ORCID,Pintor Maura^ORCID,Grosse Kathrin,Koutini Khaled^ORCID,Moser Bernhard A.,Biggio Battista^ORCID,Widmer Gerhard^ORCID

Funder

Fondazione di Sardegna

Arbeiterkammer Oberosterreich

BMK

BMDW

Ministero dell'Istruzione dell'Universita e della Ricerca

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference49 articles.

1. Adversarially robust generalization requires more data;Schmidt,2018

2. The deep bootstrap framework: good online learners are good offline generalizers;Nakkiran,2021

3. Pattern Recognition and Machine Learning;Bishop,2007

4. A kernel theory of modern data augmentation;Dao,2019

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. PatchMix: patch-level mixup for data augmentation in convolutional neural networks;Knowledge and Information Systems;2024-05-30

2. Robust hyperspectral image classification using generative adversarial networks;Information Sciences;2024-05

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