  Active learning frameworks offer efficient data annotation without remarkable
accuracy degradation. In other words, active learning starts training the model
with a small size of labeled data while exploring the space of unlabeled data
in order to select most informative samples to be labeled. Generally speaking,
representing the uncertainty is crucial in any active learning framework,
however, deep learning methods are not capable of either representing or
manipulating model uncertainty. On the other hand, from the real world
application perspective, uncertainty representation is getting more and more
attention in the machine learning community. Deep Bayesian active learning
frameworks and generally any Bayesian active learning settings, provide
practical consideration in the model which allows training with small data
while representing the model uncertainty for further efficient training. In
this paper, we briefly survey recent advances in Bayesian active learning and
in particular deep Bayesian active learning frameworks.
