论文标题
拉曼光谱与对比度表示学习
Raman Spectrum Matching with Contrastive Representation Learning
论文作者
论文摘要
拉曼光谱法是一种有效的，低成本的非侵入性技术，通常用于化学识别。典型的方法是基于将观察值与参考数据库的匹配，该观察结果需要仔细的预处理或监督的机器学习，这需要每个班级中相当大的培训观察结果。基于对比度表示学习，我们为拉曼频谱匹配提供了一种新的机器学习技术，该技术不需要预处理，并且与每个班级的单个参考谱一起工作。在三个数据集上，我们证明我们的方法在预测准确性方面显着改善或与最先进的状态相提并论，并且我们展示了如何使用指定的频繁覆盖范围来计算保形预测集。根据我们的发现，我们认为对比表示学习是Raman Spectrum匹配的现有方法的有前途的替代方法。
Raman spectroscopy is an effective, low-cost, non-intrusive technique often used for chemical identification. Typical approaches are based on matching observations to a reference database, which requires careful preprocessing, or supervised machine learning, which requires a fairly large number of training observations from each class. We propose a new machine learning technique for Raman spectrum matching, based on contrastive representation learning, that requires no preprocessing and works with as little as a single reference spectrum from each class. On three datasets we demonstrate that our approach significantly improves or is on par with the state of the art in prediction accuracy, and we show how to compute conformal prediction sets with specified frequentist coverage. Based on our findings, we believe contrastive representation learning is a promising alternative to existing methods for Raman spectrum matching.
