Uncertainty-aware complementary label queries for active learning基于主动学习的不确定性感知补标签查询

In this paper, we tackle the problem of ALCL (Liu et al., 2023). The objective of ALCL is to directly reduce the cost of annotation actions in AL, while providing a feasible approach for obtaining complementary labels. To solve ALCL, we design a sampling strategy USD, which uses the uncertainty in deep learning to guide the queries of active learning in this novel setup. Moreover, we upgrade the WEBB method to suit this sampling strategy. Comprehensive experimental results validate the performance of our proposed approaches. In the future, we plan to investigate the applicability of our approaches to large-scale datasets and account for noise in the feedback of annotators.