  We propose a new active learning strategy designed for deep neural networks.
The goal is to minimize the number of data annotation queried from an oracle
during training. Previous active learning strategies scalable for deep networks
were mostly based on uncertain sample selection. In this work, we focus on
examples lying close to the decision boundary. Based on theoretical works on
margin theory for active learning, we know that such examples may help to
considerably decrease the number of annotations. While measuring the exact
distance to the decision boundaries is intractable, we propose to rely on
adversarial examples. We do not consider anymore them as a threat instead we
exploit the information they provide on the distribution of the input space in
order to approximate the distance to decision boundaries. We demonstrate
empirically that adversarial active queries yield faster convergence of CNNs
trained on MNIST, the Shoe-Bag and the Quick-Draw datasets.
