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Mendeleev Communications, 2024, Volume 34, Issue 6, Pages 786–787
DOI: https://doi.org/10.1016/j.mencom.2024.10.006 (Mi mendc251) 		 
Communications

Contrastive representation learning for spectroscopy data analysis

A. P. Vorozhtsov, P. V. Kitina

Department of Fundamental Physical and Chemical Engineering, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
Full-text PDF (256 kB) Supplementary materials
DOI: https://doi.org/10.1016/j.mencom.2024.10.006
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.
Keywords: spectroscopy, machine learning, representation learning, neural network, metric learning, spectra analysis.
Bibliographic databases:
Document Type: Article
Language: English
Supplementary materials:
Supplementary_data_1.pdf (854.8 Kb)


Citation: A. P. Vorozhtsov, P. V. Kitina, “Contrastive representation learning for spectroscopy data analysis”, Mendeleev Commun., 34:6 (2024), 786–787
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