  Active Learning (AL) aims to enhance the performance of deep models by
selecting the most informative samples for annotation from a pool of unlabeled
data. Despite impressive performance in closed-set settings, most AL methods
fail in real-world scenarios where the unlabeled data contains unknown
categories. Recently, a few studies have attempted to tackle the AL problem for
the open-set setting. However, these methods focus more on selecting known
samples and do not efficiently utilize unknown samples obtained during AL
rounds. In this work, we propose an Entropic Open-set AL (EOAL) framework which
leverages both known and unknown distributions effectively to select
informative samples during AL rounds. Specifically, our approach employs two
different entropy scores. One measures the uncertainty of a sample with respect
to the known-class distributions. The other measures the uncertainty of the
sample with respect to the unknown-class distributions. By utilizing these two
entropy scores we effectively separate the known and unknown samples from the
unlabeled data resulting in better sampling. Through extensive experiments, we
show that the proposed method outperforms existing state-of-the-art methods on
CIFAR-10, CIFAR-100, and TinyImageNet datasets. Code is available at
\url{https://github.com/bardisafa/EOAL}.
