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

TY - JOUR

T1 - Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy

T2 - Generative Adversarial Networks

AU - del-Pozo-Bueno, Daniel

AU - Kepaptsoglou, Demie

AU - Ramasse, Quentin M

AU - Peiró, Francesca

AU - Estradé, Sònia

N1 - © The Author(s) 2024.

PY - 2024/4/29

Y1 - 2024/4/29

N2 - 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.

AB - 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.

U2 - 10.1093/mam/ozae014

DO - 10.1093/mam/ozae014

M3 - Article

SN - 1431-9276

VL - 30

SP - 278

EP - 293

JO - Microscopy and Microanalysis

JF - Microscopy and Microanalysis

IS - 2

ER -