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Addressing Data Scarcity in Spectroscopy with Variational Autoencodersстатья Исследовательская статья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 23 января 2026 г.
- Авторы: Mushchina A., Isaev I., Sarmanova O., Dolenko T., Dolenko S.
- Журнал: Optical Memory and Neural Networks (Information Optics)
- Том: 34
- Номер: Suppl.1
- Год издания: 2025
- Издательство: Allerton Press Inc.
- Местоположение издательства: United States
- Первая страница: S115
- Последняя страница: S124
- DOI: 10.3103/S1060992X25700201
- Аннотация: Solving inverse problems in many areas of natural science, including spectroscopy, is often a challenge due to well-known properties of such problems, including nonlinearity, high input dimension, and being ill-posed or incorrect. One of the approaches that may deal with these problems is the use of machine learning methods, e.g. artificial neural networks. However, machine learning methods require a large amount of representative data, which is often hard and expensive to obtain in experiment. An alternative may be generation of additional data with generative neural network systems, e.g. variational autoencoders. In this study, we investigate feasibility of such approach, its merits and difficulties of its use at the example of optical absorption spectroscopy of multicomponent solutions of inorganic salts applied to determine the concentrations of the components of a solution.
- Добавил в систему: Доленко Сергей Анатольевич
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