  In this work, we introduce a novel framework that employs cluster annotation
to boost active learning by reducing the number of human interactions required
to train deep neural networks. Instead of annotating single samples
individually, humans can also label clusters, producing a higher number of
annotated samples with the cost of a small label error. Our experiments show
that the proposed framework requires 82% and 87% less human interactions for
CIFAR-10 and EuroSAT datasets respectively when compared with the
fully-supervised training while maintaining similar performance on the test
set.
