  Active learning aims to optimize the dataset annotation process when
resources are constrained. Most existing methods are designed for balanced
datasets. Their practical applicability is limited by the fact that a majority
of real-life datasets are actually imbalanced. Here, we introduce a new active
learning method which is designed for imbalanced datasets. It favors samples
likely to be in minority classes so as to reduce the imbalance of the labeled
subset and create a better representation for these classes. We also compare
two training schemes for active learning: (1) the one commonly deployed in deep
active learning using model fine tuning for each iteration and (2) a scheme
which is inspired by transfer learning and exploits generic pre-trained models
and train shallow classifiers for each iteration. Evaluation is run with three
imbalanced datasets. Results show that the proposed active learning method
outperforms competitive baselines. Equally interesting, they also indicate that
the transfer learning training scheme outperforms model fine tuning if features
are transferable from the generic dataset to the unlabeled one. This last
result is surprising and should encourage the community to explore the design
of deep active learning methods.
