论文标题
利用无监督的数据和域的适应性，以进行低成本传感器校准中的深层回归
Leveraging unsupervised data and domain adaptation for deep regression in low-cost sensor calibration
论文作者
论文摘要
随着对空气质量的认识，空气质量监测已成为一项重要任务。低成本的空气质量传感器易于部署，但不如昂贵且笨重的参考监视器可靠。在深度学习的帮助下，可以针对参考监视器校准低质量的传感器。在本文中，我们将传感器校准的任务转化为半监督的域适应性问题，并为此提出了一种新颖的解决方案。这个问题是具有挑战性的，因为它是协变量转移和标签差距的回归问题。我们使用直方图丢失而不是均值或平均绝对误差，通常用于回归，并发现它可以在协变速器上进行。为了处理标签间隙，我们提出样品的加权以进行对抗熵优化。在实验评估中，提出的方案的表现优于许多竞争基线，这些基线基于半监督和监督的域适应性，其基于R2分数和平均绝对误差。消融研究表明了整个方案中每个提出的组件的相关性。
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.
