  Unlabeled data examples awaiting annotations contain open-set noise
inevitably. A few active learning studies have attempted to deal with this
open-set noise for sample selection by filtering out the noisy examples.
However, because focusing on the purity of examples in a query set leads to
overlooking the informativeness of the examples, the best balancing of purity
and informativeness remains an important question. In this paper, to solve this
purity-informativeness dilemma in open-set active learning, we propose a novel
Meta-Query-Net,(MQ-Net) that adaptively finds the best balancing between the
two factors. Specifically, by leveraging the multi-round property of active
learning, we train MQ-Net using a query set without an additional validation
set. Furthermore, a clear dominance relationship between unlabeled examples is
effectively captured by MQ-Net through a novel skyline regularization.
Extensive experiments on multiple open-set active learning scenarios
demonstrate that the proposed MQ-Net achieves 20.14% improvement in terms of
accuracy, compared with the state-of-the-art methods.
