Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks

del-Pozo-Bueno, D. orcid.org/0000-0003-1819-298X, Kepaptsoglou, D. orcid.org/0000-0003-0499-0470, Ramasse, Q.M. orcid.org/0000-0001-7466-2283 et al. (2 more authors) (2024) Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks. Microscopy and Microanalysis, 30 (2). pp. 278-293. ISSN 1431-9276

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Item Type: Article
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© The Author(s) 2024. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY-NC 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: data augmentation, electron energy loss spectroscopy, generative adversarial networks, machine learning, support vector machines
Dates:
  • Published: 29 April 2024
  • Published (online): 29 April 2024
  • Accepted: 12 February 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 07 Jun 2024 11:09
Last Modified: 07 Jun 2024 11:09
Status: Published
Publisher: Oxford University Press
Identification Number: 10.1093/mam/ozae014
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