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Air quality monitoring is becoming an essential task with rising awarenessabout air quality. Low cost air quality sensors are easy to deploy but are notas reliable as the costly and bulky reference monitors. The low quality sensorscan be calibrated against the reference monitors with the help of deeplearning. In this paper, we translate the task of sensor calibration into asemi-supervised domain adaptation problem and propose a novel solution for thesame. The problem is challenging because it is a regression problem withcovariate shift and label gap. We use histogram loss instead of mean squared ormean absolute error, which is commonly used for regression, and find it usefulagainst covariate shift. To handle the label gap, we propose weighting ofsamples for adversarial entropy optimization. In experimental evaluations, theproposed scheme outperforms many competitive baselines, which are based onsemi-supervised and supervised domain adaptation, in terms of R2 score and meanabsolute error. Ablation studies show the relevance of each proposed componentin the entire scheme.

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