Partial Label Learning meets Active Learning: Enhancing Annotation Efficiency through Binary Questioning

Supervised learning is an effective approach to machine learning, but it can be expensive to acquire labeled data. Active learning (AL) and partial label learning (PLL) are two techniques that can be used to reduce the annotation costs of supervised learning. AL is a strategy for reducing the annotation budget by selecting and labeling the most informative samples, while PLL is a weakly supervised learning approach to learn from partially annotated data by identifying the true hidden label. In this paper, we propose a novel approach that combines AL and PLL techniques to improve annotation efficiency. Our method leverages AL to select informative binary questions and PLL to identify the true label from the set of possible answers. We conduct extensive experiments on various benchmark datasets and show that our method achieves state-of-the-art (SoTA) performance with significantly reduced annotation costs. Our findings suggest that our method is a promising solution for cost-effective annotation in real-world applications.