  We investigate different strategies for active learning with Bayesian deep
neural networks. We focus our analysis on scenarios where new, unlabeled data
is obtained episodically, such as commonly encountered in mobile robotics
applications. An evaluation of different strategies for acquisition, updating,
and final training on the CIFAR-10 dataset shows that incremental network
updates with final training on the accumulated acquisition set are essential
for best performance, while limiting the amount of required human labeling
labor.
