Super-resolution of Time-series Labels for Bootstrapped Event Detection

Here, weak labels refer to labels that indicate one deep learning, relies on the availability of abundant, or more events are present in the sample, although do not quality data. In this paper we develop a contain the information as to the event frequency nor the novel framework that maximises the utility of exact location of occurrence(s) (illustrated in Section 2.2, time-series datasets that contain only small quantities Figure 1). Our goal therefore is to improve classification of expertly-labelled data, larger quantities performance in domains with variable quality datasets. of weakly (or coarsely) labelled data and a large volume of unlabelled data. This represents scenarios Our key contribution is as follows. We propose a framework commonly encountered in the real world, that combines the strengths of both traditional algorithms such as in crowd-sourcing applications. In our and deep learning methods, to perform multi-resolution work, we use a nested loop using a Kernel Density Bayesian bootstrapping. We obtain probabilistic labels for Estimator (KDE) to super-resolve the abundant pseudo-fine labels, generated from weak labels, which can low-quality data labels, thereby enabling effective then be used to train a neural network. For the label refinement training of a Convolutional Neural Network from weak to fine we use a Kernel Density Estimator (CNN). We demonstrate two key results: (KDE).