  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.
