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.

在本文中，我们解决了 ALCL 问题（Liu et al., 2023）。ALCL 的目标是直接降低 AL 中注释操作的成本，同时提供获取互补标签的可行方法。为了解决 ALCL，我们设计了一种采样策略 USD，它利用深度学习中的不确定性来指导这种新颖设置中主动学习的查询。此外，我们升级了 WEBB 方法以适应这种采样策略。综合实验结果验证了我们提出的方法的性能。未来，我们计划研究我们的方法对大规模数据集的适用性，并解释注释者反馈中的噪声。