  Active learning is able to reduce the amount of labelling effort by using a
machine learning model to query the user for specific inputs.
  While there are many papers on new active learning techniques, these
techniques rarely satisfy the constraints of a real-world project. In this
paper, we analyse the main drawbacks of current active learning techniques and
we present approaches to alleviate them. We do a systematic study on the
effects of the most common issues of real-world datasets on the deep active
learning process: model convergence, annotation error, and dataset imbalance.
We derive two techniques that can speed up the active learning loop such as
partial uncertainty sampling and larger query size. Finally, we present our
open-source Bayesian active learning library, BaaL.
