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arXiv · 2302.00299

Learning from Stochastic Labels

Meng Wei·Zhongnian Li·Yong Zhou·Qiaoyu Guo·Xinzheng Xu
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Abstract

Annotating multi-class instances is a crucial task in the field of machine learning. Unfortunately, identifying the correct class label from a long sequence of candidate labels is time-consuming and laborious. To alleviate this problem, we design a novel labeling mechanism called stochastic label. In this setting, stochastic label includes two cases: 1) identify a correct class label from a small number of randomly given labels; 2) annotate the instance with None label when given labels do not contain correct class label. In this paper, we propose a novel suitable approach to learn from these stochastic labels. We obtain an unbiased estimator that utilizes less supervised information in stochastic labels to train a multi-class classifier. Additionally, it is theoretically justifiable by deriving the estimation error bound of the proposed method. Finally, we conduct extensive experiments on widely-used benchmark datasets to validate the superiority of our method by comparing it with existing state-of-the-art methods.

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Meng Wei, Zhongnian Li, Yong Zhou, Qiaoyu Guo, Xinzheng Xu. 2023-02-01. Learning from Stochastic Labels. https://doi.org/10.1109/icme59968.2025.11209869

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