  Deep learning models have demonstrated outstanding performance in several
problems, but their training process tends to require immense amounts of
computational and human resources for training and labeling, constraining the
types of problems that can be tackled. Therefore, the design of effective
training methods that require small labeled training sets is an important
research direction that will allow a more effective use of resources.Among
current approaches designed to address this issue, two are particularly
interesting: data augmentation and active learning. Data augmentation achieves
this goal by artificially generating new training points, while active learning
relies on the selection of the "most informative" subset of unlabeled training
samples to be labelled by an oracle. Although successful in practice, data
augmentation can waste computational resources because it indiscriminately
generates samples that are not guaranteed to be informative, and active
learning selects a small subset of informative samples (from a large
un-annotated set) that may be insufficient for the training process. In this
paper, we propose a Bayesian generative active deep learning approach that
combines active learning with data augmentation -- we provide theoretical and
empirical evidence (MNIST, CIFAR-$\{10,100\}$, and SVHN) that our approach has
more efficient training and better classification results than data
augmentation and active learning.
