Contrastive representation learning for spectroscopy data analysis

被引：0
|
作者：
Vorozhtsov, Artem P. [1 ]
V. Kitina, Polina [1 ]
机构：
[1] MV Lomonosov Moscow State Univ, Dept Fundamental Phys & Chem Engn, Moscow 119991, Russia
来源：
MENDELEEV COMMUNICATIONS | 2024年 / 34卷 / 06期
关键词：
spectroscopy; machine learning; representation learning; neural network; metric learning; NEURAL-NETWORKS; RECOGNITION;
D O I：
10.1016/j.mencom.2024.10.006
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
Metric-based representation learning showed good accuracy in identifying objects from one-dimensional spectroscopy data, robustness to small dataset size and the ability change the data domain without fine-tuning.
引用
收藏
页码：786 / 787
页数：2
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