Bidirectional Uncertainty-Based Active Learning for Open Set Annotation
Chen-Chen Zong Ye-Wen Wang Kun-Peng Ning Haibo Ye Sheng-Jun Huang
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
\textit{Bidirectional Uncertainty-based Active Learning} (BUAL) framework.
Specifically, we achieve this by first pushing the unknown class examples
toward regions with high-confidence predictions with our proposed
\textit{Random Label Negative Learning} method. Then, we propose a
\textit{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.