{"title":"Contrastive representation learning for spectroscopy data analysis","authors":"Artem P. Vorozhtsov, Polina V. Kitina","doi":"10.1016/j.mencom.2024.10.006","DOIUrl":null,"url":null,"abstract":"<div><div>Metric-based representation learning showed good accuracy in identifying objects from one-dimensional spectroscopy data, robustness to small dataset size and the ability to change the data domain without fine-tuning.</div></div>","PeriodicalId":18542,"journal":{"name":"Mendeleev Communications","volume":"34 6","pages":"Pages 786-787"},"PeriodicalIF":1.8000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mendeleev Communications","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0959943624003043","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Metric-based representation learning showed good accuracy in identifying objects from one-dimensional spectroscopy data, robustness to small dataset size and the ability to change the data domain without fine-tuning.
期刊介绍：
Mendeleev Communications is the journal of the Russian Academy of Sciences, launched jointly by the Academy of Sciences of the USSR and the Royal Society of Chemistry (United Kingdom) in 1991. Starting from 1st January 2007, Elsevier is the new publishing partner of Mendeleev Communications.
Mendeleev Communications publishes short communications in chemistry. The journal primarily features papers from the Russian Federation and the other states of the former USSR. However, it also includes papers by authors from other parts of the world. Mendeleev Communications is not a translated journal, but instead is published directly in English. The International Editorial Board is composed of eminent scientists who provide advice on refereeing policy.
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