  We introduce Information Condensing Active Learning (ICAL), a batch mode
model agnostic Active Learning (AL) method targeted at Deep Bayesian Active
Learning that focuses on acquiring labels for points which have as much
information as possible about the still unacquired points. ICAL uses the
Hilbert Schmidt Independence Criterion (HSIC) to measure the strength of the
dependency between a candidate batch of points and the unlabeled set. We
develop key optimizations that allow us to scale our method to large unlabeled
sets. We show significant improvements in terms of model accuracy and negative
log likelihood (NLL) on several image datasets compared to state of the art
batch mode AL methods for deep learning.
