# Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy

arXiv: https://arxiv.org/abs/2104.03439 · HTML: https://mlsanity.com/paper/2104.03439/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 8 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)

## Built on by (most cited first, 4 of 4)

- [A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness](https://mlsanity.com/paper/2106.09129/) (2021)
- [Sparse Binary Transformers for Multivariate Time Series Modeling](https://mlsanity.com/paper/2308.04637/) (2023)
- [Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices](https://mlsanity.com/paper/2203.11295/) (2022)
- [PGNAA Spectral Classification of Metal with Density Estimations](https://mlsanity.com/paper/2208.13836/) (2022)

## Abstract (arXiv)

Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elemental analysis technique used to determine the chemical composition of target samples, such as in industrial analysis of metals or in space exploration. Recently, there has been a rise in the use of machine learning (ML) techniques for LIBS data processing. However, ML for LIBS is challenging as: (i) the predictive models must be lightweight since they need to be deployed in highly resource-constrained and battery-operated portable LIBS systems; and (ii) since these systems can be remote, the models must be able to self-adapt to any domain shift in input distributions which could be due to the lack of different types of inputs in training data or dynamic environmental/sensor noise. This on-device retraining of model should not only be fast but also unsupervised due to the absence of new labeled data in remote LIBS systems. We introduce a lightweight multi-layer perceptron (MLP) model for LIBS that can be adapted on-device without requiring labels for new input data. It shows 89.3% average accuracy during data streaming, and up to 2.1% better accuracy compared to an MLP model that does not support adaptation. Finally, we also characterize the inference and retraining performance of our model on Google Pixel2 phone.