  We present geometric Bayesian active learning by disagreements (GBALD), a
framework that performs BALD on its core-set construction interacting with
model uncertainty estimation. Technically, GBALD constructs core-set on
ellipsoid, not typical sphere, preventing low-representative elements from
spherical boundaries. The improvements are twofold: 1) relieve uninformative
prior and 2) reduce redundant estimations. Theoretically, geodesic search with
ellipsoid can derive tighter lower bound on error and easier to achieve zero
error than with sphere. Experiments show that GBALD has slight perturbations to
noisy and repeated samples, and outperforms BALD, BatchBALD and other existing
deep active learning approaches.
