# Few-shot calibration of low-cost air pollution (PM2.5) sensors using meta-learning

arXiv: https://arxiv.org/abs/2108.00640 · HTML: https://mlsanity.com/paper/2108.00640/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 2 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Meta-Transfer Learning for Few-Shot Learning](https://mlsanity.com/paper/1812.02391/) (2018)
- [A Comprehensive Analysis of Deep Regression](https://mlsanity.com/paper/1803.08450/) (2018)

## Built on by (most cited first, 3 of 3)

- [Leveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibration](https://mlsanity.com/paper/2210.00521/) (2022)
- [Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions](https://mlsanity.com/paper/2404.07308/) (2024)
- [A temporal deep learning framework for calibration of low-cost air quality sensors](https://mlsanity.com/paper/2604.21527/) (2026)

## Abstract (arXiv)

Low-cost particulate matter sensors are transforming air quality monitoring because they have lower costs and greater mobility as compared to reference monitors. Calibration of these low-cost sensors requires training data from co-deployed reference monitors. Machine Learning based calibration gives better performance than conventional techniques, but requires a large amount of training data from the sensor, to be calibrated, co-deployed with a reference monitor. In this work, we propose novel transfer learning methods for quick calibration of sensors with minimal co-deployment with reference monitors. Transfer learning utilizes a large amount of data from other sensors along with a limited amount of data from the target sensor. Our extensive experimentation finds the proposed Model-Agnostic- Meta-Learning (MAML) based transfer learning method to be the most effective over other competitive baselines.