Leveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibration

by   Swapnil Dey, et al.

Air quality monitoring is becoming an essential task with rising awareness about air quality. Low cost air quality sensors are easy to deploy but are not as reliable as the costly and bulky reference monitors. The low quality sensors can be calibrated against the reference monitors with the help of deep learning. In this paper, we translate the task of sensor calibration into a semi-supervised domain adaptation problem and propose a novel solution for the same. The problem is challenging because it is a regression problem with covariate shift and label gap. We use histogram loss instead of mean squared or mean absolute error, which is commonly used for regression, and find it useful against covariate shift. To handle the label gap, we propose weighting of samples for adversarial entropy optimization. In experimental evaluations, the proposed scheme outperforms many competitive baselines, which are based on semi-supervised and supervised domain adaptation, in terms of R2 score and mean absolute error. Ablation studies show the relevance of each proposed component in the entire scheme.


page 1

page 2

page 3

page 4


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

Low-cost particulate matter sensors are transforming air quality monitor...

Low Cost Sensor Networks; How Do We Know the Data are Reliable?

Plausibility of data from networks of low-cost measurement devices is a ...

A Dual Adversarial Calibration Framework for Automatic Fetal Brain Biometry

This paper presents a novel approach to automatic fetal brain biometry m...

Adversarial Weighting for Domain Adaptation in Regression

We present a novel instance based approach to handle regression tasks in...

MTNet: A Multi-Task Neural Network for On-Field Calibration of Low-Cost Air Monitoring Sensors

The advances of sensor technology enable people to monitor air quality t...

Semi-supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Accurate building height estimation is key to the automatic derivation o...

Please sign up or login with your details

Forgot password? Click here to reset