Solving real-world problems, particularly with deep learning, relies on the availability of abundant, quality data. In this paper we develop a novel framework that maximises the utility of time-series datasets that contain only small quantities of expertly-labelled data, larger quantities of weakly (or coarsely) labelled data and a large volume of unlabelled data. This represents scenarios commonly encountered in the real world, such as in crowd-sourcing applications. In our work, we use a nested loop using a Kernel Density Estimator (KDE) to super-resolve the abundant low-quality data labels, thereby enabling effective training of a Convolutional Neural Network (CNN). We demonstrate two key results: a) The KDE is able to super-resolve labels more accurately, and with better calibrated probabilities, than well-established classifiers acting as baselines; b) Our CNN, trained on super-resolved labels from the KDE, achieves an improvement in F1 score of 22.1% over the next best baseline system in our candidate problem domain.

本研究提出了一种新的框架，通过使用核密度估计器和卷积神经网络实现仅有少量标注数据、大量弱标注数据和大量未标注数据的时序数据集的训练。实验结果表明，使用核密度估计器进行超分辨率可以更准确、更具有校准性地实现标注，训练后的卷积神经网络在候选问题领域中比下一个最佳基线系统的F1分数提高22.1%。

用于自举事件检测的时间序列标签超分辨率