del-Pozo-Bueno, Daniel, Kepaptsoglou, Demie orcid.org/0000-0003-0499-0470, Ramasse, Quentin M et al. (2 more authors) (2024) Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy:Generative Adversarial Networks. Microscopy and Microanalysis. 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. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Physics (York) |
Depositing User: | Pure (York) |
Date Deposited: | 03 May 2024 16:00 |
Last Modified: | 16 Oct 2024 19:56 |
Published Version: | https://doi.org/10.1093/mam/ozae014 |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1093/mam/ozae014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212256 |
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Description: Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks
Licence: CC-BY-NC 2.5