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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/217911
Machine learning data augmentation strategy for electron energy loss spectroscopy: generative adversarial networks
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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.
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BUENO DEL POZO, Daniel, YEDRA, Lluis, KEPAPTSOGLOU, Demie, RAMASSE, Quentin, PEIRÓ MARTÍNEZ, Francisca, ESTRADÉ ALBIOL, Sònia. Machine learning data augmentation strategy for electron energy loss spectroscopy: generative adversarial networks. _Microscopy and Microanalysis_. 2024. Vol. 30, núm. 278-293. [consulta: 7 de febrer de 2026]. ISSN: 1431-9276. [Disponible a: https://hdl.handle.net/2445/217911]