  When we can not assume a large amount of annotated data , active learning is
a good strategy. It consists in learning a model on a small amount of annotated
data (annotation budget) and in choosing the best set of points to annotate in
order to improve the previous model and gain in generalization. In deep
learning, active learning is usually implemented as an iterative process in
which successive deep models are updated via fine tuning, but it still poses
some issues. First, the initial batch of annotated images has to be
sufficiently large to train a deep model. Such an assumption is strong,
especially when the total annotation budget is reduced. We tackle this issue by
using an approach inspired by transfer learning. A pre-trained model is used as
a feature extractor and only shallow classifiers are learned during the active
iterations. The second issue is the effectiveness of probability or feature
estimates of early models for AL task. Samples are generally selected for
annotation using acquisition functions based only on the last learned model. We
introduce a novel acquisition function which exploits the iterative nature of
AL process to select samples in a more robust fashion. Samples for which there
is a maximum shift towards uncertainty between the last two learned models
predictions are favored. A diversification step is added to select samples from
different regions of the classification space and thus introduces a
representativeness component in our approach. Evaluation is done against
competitive methods with three balanced and imbalanced datasets and outperforms
them.
