  We consider active learning of deep neural networks. Most active learning
works in this context have focused on studying effective querying mechanisms
and assumed that an appropriate network architecture is a priori known for the
problem at hand. We challenge this assumption and propose a novel active
strategy whereby the learning algorithm searches for effective architectures on
the fly, while actively learning. We apply our strategy using three known
querying techniques (softmax response, MC-dropout, and coresets) and show that
the proposed approach overwhelmingly outperforms active learning using fixed
architectures.
