[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127043-en":3,"doc-seo-127043-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127043,962084928904,"Asher","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy - Generative Adversarial Networks","Recent advances in machine learning (ML) have emphasized a key limitation: supervised learning requires both high-quality and sufficient quantities of labeled data. This article proposes a data augmentation strategy for electron energy loss spectroscopy (EELS) using generative adversarial networks (GANs). The approach, termed DAG, enables realistic spectrum generation from only ~100 spectra. GAN configurations are systematically explored to obtain the most realistic outputs, which then train classifiers based on ANNs and SVMs for classifying experimental EELS spectra.","Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks  \nDaniel del-Pozo-Bueno, Demie Kepaptsoglou, Quentin M Ramasse, Francesca Peiró, Sònia Estradé  \nDownloaded from [https://academic.oup.com/mam/article/30/2/278/7659785 by UGI Biblioteca Lletres user on 28 October 2024](https://academic.oup.com/mam/article/30/2/278/7659785 by UGI Biblioteca Lletres user on 28 October 2024)  \nMicroscopy and Microanalysis, 2024, 30, 278–293 [https://doi.org/10.1093/mam/ozae014](https://doi.org/10.1093/mam/ozae014)  \nOriginal Article  \nMachine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy: Generative Adversarial Networks  \nDaniel del-Pozo-Bueno 1,2, *, Demie Kepaptsoglou3,4, Quentin M. Ramasse3,5, Francesca Peiró 1,2, and Sònia Estradé 1,2  \n1LENS-MIND, Departament d’Enginyeria Electrònica i Biomèdica, Universitat de Barcelona, 1-11 Martí i Franquès, 08028 Barcelona, Spain 2Institute of Nanoscience and Nanotechnology (IN2UB), Universitat de Barcelona, 1-11 Martí i Franquès, 08028 Barcelona, Spain 3SuperSTEM Laboratory, Sci-Tech Daresbury Campus, Keckwick Lane, Daresbury WA4 4AD, UK  \n4School of Physics, Engineering and Technology, University of York, Newton way, YO10 5DD Heslington, UK  \n5Schools of Chemical and Process Engineering & Physics and Astronomy, Woodhouse Lane, University of Leeds, LS2 9JT Leeds, UK  \n*Corresponding author: Daniel del-Pozo-Bueno, E-mail: [dpb.523@gmail.com](dpb.523@gmail.com)  \nAbstract  \nRecent 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.  \nKey words: data augmentation, electron energy loss spectroscopy, generative adversarial networks, machine learning, support vector machines  \nIntroduction  \nSince the advent of spherical aberration correctors in the early 2000s, there have been substantial improvements in both spatial and energy resolutions of scanning and transmission electron microscopy (S/TEM) and electron energy loss spectroscopy (EELS) techniques because of several technological advancements, such as monochromators, direct detectors, and increased computational power. However, another challenge has arisen from these instrumental enhancements: the generation of vast quantities of data per acquisition. As a result, there is a growing recognition within the EELS community of the necessity for new data analysis tools, such as machine learning (ML) algorithms, to accurately and efficiently process this abundance of data.  \nWhen it comes to the confluence of ML and EELS, the current state of the art reveals that the applied strategies can be broadly categorized into two main approaches: supervised and unsupervised. Supervised approaches involve training ML models using labeled data, where the desired output or target is known. These models learn from the labeled examples to make predictions or classify new, unseen data points. In the context of EELS, supervised ML algorithms can be employed for specific tasks such as material identification and elemental mapping by leveraging prelabeled training data. Supervised  \nstrategies based on support vector machines","cbCaifU8C41tVYXM","https://ap.wps.com/l/cbCaifU8C41tVYXM","pdf",5411839,1,18,"English","en",105,"# Abstract\n# Introduction\n## Supervised ML approaches in EELS\n## Unsupervised ML approaches in EELS","[{\"question\":\"What problem does the data augmentation strategy address in supervised machine learning for EELS?\",\"answer\":\"It addresses the need for high-quality and sufficient labeled data to effectively train supervised ML models. The proposed strategy generates additional realistic EELS spectra to mitigate limited dataset size.\"},{\"question\":\"What is DAG in this article?\",\"answer\":\"DAG stands for the data augmentation generative adversarial network. It is used to generate realistic EELS spectra from a very limited number of input spectra (around 100).\"},{\"question\":\"How are the generated spectra used after training the GAN?\",\"answer\":\"The generated spectra are used to train classifiers, including artificial neural networks (ANNs) and support vector machines (SVMs), which are applied to classify experimental EELS spectra.\"}]","Machine Learning Data Augmentation Strategy for Electron Energy Loss Spectroscopy - Generative Adversarial Networks | PDF",1785936518,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-data-augmentation-strategy-for-electron-energy-loss-spectroscopy-generative-adversarial-networks","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-data-augmentation-strategy-for-electron-energy-loss-spectroscopy-generative-adversarial-networks/127043/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the data augmentation strategy address in supervised machine learning for EELS?","Question",{"text":75,"@type":76},"It addresses the need for high-quality and sufficient labeled data to effectively train supervised ML models. The proposed strategy generates additional realistic EELS spectra to mitigate limited dataset size.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is DAG in this article?",{"text":80,"@type":76},"DAG stands for the data augmentation generative adversarial network. It is used to generate realistic EELS spectra from a very limited number of input spectra (around 100).",{"name":82,"@type":73,"acceptedAnswer":83},"How are the generated spectra used after training the GAN?",{"text":84,"@type":76},"The generated spectra are used to train classifiers, including artificial neural networks (ANNs) and support vector machines (SVMs), which are applied to classify experimental EELS spectra.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]