Learning from Stochastic Labels
Meng Wei, Xinzheng Xu, Peng Ying, Renke Sun, Guanjun Wang, Zhongnian Li · 2025
To reduce pressure of manual annotation, researchers have explored various weakly supervised learning methods and achieved remarkable results in multi-class classification tasks. However, these methods still require annotating from the entire set of candidate labels, which becomes particularly time-consuming when the labeling space is large. To alleviate this problem, we propose a novel labeling mechanism called stochastic labels, which reduces the time spent browsing labeling space by annotating the instance from a small labels subset. In this paper, we introduce an unbiased risk estimator and establish a prototype baseline to learn a multi-class classifier from these stochastic labels. Besides, we derive the estimation error bound of the proposed method, showing that the empirical risk could converge to the true classification risk as the number of training samples increases. Finally, we conduct extensive experiments on widely-used benchmark datasets to validate the effectiveness of our approach. Our method surpasses state-of-the-art weakly supervised methods, highlighting its efficiency and robustness. Our code is available at: https://github.com/WilsonMqz/SLL