  Active learning aims to develop label-efficient algorithms by querying the
most informative samples to be labeled by an oracle. The design of efficient
training methods that require fewer labels is an important research direction
that allows more effective use of computational and human resources for
labeling and training deep neural networks. In this work, we demonstrate how we
can use recent advances in deep generative models, to outperform the
state-of-the-art in achieving the highest classification accuracy using as few
labels as possible. Unlike previous approaches, our approach uses not only
labeled images to train the classifier but also unlabeled images and generated
images for co-training the whole model. Our experiments show that the proposed
method significantly outperforms existing approaches in active learning on a
wide range of datasets (MNIST, CIFAR-10, SVHN, CelebA, and ImageNet).
