  Active learning has proven to be useful for minimizing labeling costs by
selecting the most informative samples. However, existing active learning
methods do not work well in realistic scenarios such as imbalance or rare
classes, out-of-distribution data in the unlabeled set, and redundancy. In this
work, we propose SIMILAR (Submodular Information Measures based actIve
LeARning), a unified active learning framework using recently proposed
submodular information measures (SIM) as acquisition functions. We argue that
SIMILAR not only works in standard active learning, but also easily extends to
the realistic settings considered above and acts as a one-stop solution for
active learning that is scalable to large real-world datasets. Empirically, we
show that SIMILAR significantly outperforms existing active learning algorithms
by as much as ~5% - 18% in the case of rare classes and ~5% - 10% in the case
of out-of-distribution data on several image classification tasks like
CIFAR-10, MNIST, and ImageNet. SIMILAR is available as a part of the DISTIL
toolkit: "https://github.com/decile-team/distil".
