In active learning, acquisition functions define informativeness directly on the representation position within model manifold. However, for most machine learning models (in particular neural networks) this is not fixed due to training pool fluctuations in between rounds. Therefore, several popular strategies are sensitive experiment parameters (e.g. architecture) and do consider robustness out...