# Raman Spectrum Matching with Contrastive Representation Learning

arXiv: https://arxiv.org/abs/2202.12549 · HTML: https://mlsanity.com/paper/2202.12549/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 36 references that are in the map.
- First posted on arXiv 25 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Classification with Valid and Adaptive Coverage](https://mlsanity.com/paper/2006.02544/) (2020)
- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks](https://mlsanity.com/paper/1511.06314/) (2015)
- [A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation](https://mlsanity.com/paper/1810.13243/) (2018)
- [Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution](https://mlsanity.com/paper/1708.09022/) (2017)
- [Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning](https://mlsanity.com/paper/1901.07666/) (2019)
- [Dynamic Spectrum Matching with One-shot Learning](https://mlsanity.com/paper/1806.09981/) (2018)
- [Feature visualization of Raman spectrum analysis with deep convolutional neural network](https://mlsanity.com/paper/2007.13354/) (2020)

## Built on by (most cited first, 2 of 2)

- [Machine learning identification of organic compounds using visible light](https://mlsanity.com/paper/2204.11832/) (2022)
- [From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data](https://mlsanity.com/paper/2411.11693/) (2024)

## Abstract (arXiv)

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.