  The high cost of acquiring labels is one of the main challenges in deploying
supervised machine learning algorithms. Active learning is a promising approach
to control the learning process and address the difficulties of data labeling
by selecting labeled training examples from a large pool of unlabeled
instances. In this paper, we propose a new data-driven approach to active
learning by choosing a small set of labeled data points that are both
informative and representative. To this end, we present an efficient geometric
technique to select a diverse core-set in a low-dimensional latent space
obtained by training a Variational Autoencoder (VAE). Our experiments
demonstrate an improvement in accuracy over two related techniques and, more
importantly, signify the representation power of generative modeling for
developing new active learning methods in high-dimensional data settings.
