  Active Learning is concerned with the question of how to identify the most
useful samples for a Machine Learning algorithm to be trained with. When
applied correctly, it can be a very powerful tool to counteract the immense
data requirements of Artificial Neural Networks. However, we find that it is
often applied with not enough care and domain knowledge. As a consequence,
unrealistic hopes are raised and transfer of the experimental results from one
dataset to another becomes unnecessarily hard.
  In this work we analyse the robustness of different Active Learning methods
with respect to classifier capacity, exchangeability and type, as well as
hyperparameters and falsely labelled data. Experiments reveal possible biases
towards the architecture used for sample selection, resulting in suboptimal
performance for other classifiers. We further propose the new "Sum of Squared
Logits" method based on the Simpson diversity index and investigate the effect
of using the confusion matrix for balancing in sample selection.
