  Active learning (AL) in open set scenarios presents a novel challenge of
identifying the most valuable examples in an unlabeled data pool that comprises
data from both known and unknown classes. Traditional methods prioritize
selecting informative examples with low confidence, with the risk of mistakenly
selecting unknown-class examples with similarly low confidence. Recent methods
favor the most probable known-class examples, with the risk of picking simple
already mastered examples. In this paper, we attempt to query examples that are
both likely from known classes and highly informative, and propose a
Bidirectional Uncertainty-based Active Learning (BUAL) framework. Specifically,
we achieve this by first pushing the unknown class examples toward regions with
high-confidence predictions, i.e., the proposed Random Label Negative Learning
method. Then, we propose a Bidirectional Uncertainty sampling strategy by
jointly estimating uncertainty posed by both positive and negative learning to
perform consistent and stable sampling. BUAL successfully extends existing
uncertainty-based AL methods to complex open-set scenarios. Extensive
experiments on multiple datasets with varying openness demonstrate that BUAL
achieves state-of-the-art performance. The code is available at
https://github.com/chenchenzong/BUAL.
