Active learning for time instant classification

Active learning is a common strategy for reducing the dependency of model training on large labeled datasets by selecting only the most useful data for labeling. In this work, we consider the problem of actively selecting labels for time instant classification using neural network classifiers. We propose a novel method that selects samples based on a combination of factors that includes uncertainty, diversity, and data density. The performance of the proposed method is demonstrated on synthetic and robot activity datasets.