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
Abstract
Recent advances in machine learning (ML) have highlighted a novel challenge concerning the quality and quantity of data required to effectively train algorithms in supervised ML procedures. This article introduces a data augmentation (DA) strategy for electron energy loss spectroscopy (EELS) data, employing generative adversarial networks (GANs). We present an innovative approach, called the data augmentation generative adversarial network (DAG), which facilitates data generation from a very limited number of spectra, around 100. Throughout this study, we explore the optimal configuration for GANs to produce realistic spectra. Notably, our DAG generates realistic spectra, and the spectra produced by the generator are successfully used in real-world applications to train classifiers based on artificial neural networks (ANNs) and support vector machines (SVMs) that have been successful in classifying experimental EEL spectra.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213184 |