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Imprecise label learning: A unified framework for learning with various imprecise label configurations
Chen, Hao Shah, Ankit Wang, Jindong Tao, Ran Wang, Yidong Li, Xiang Xie, Xing Sugiyama, Masashi Singh, Rita Raj, Bhiksha
Chen, Hao
Shah, Ankit
Wang, Jindong
Tao, Ran
Wang, Yidong
Li, Xiang
Xie, Xing
Sugiyama, Masashi
Singh, Rita
Raj, Bhiksha
Supervisor
Department
Natural Language Processing
Embargo End Date
Type
Conference proceeding
Date
2024
License
Language
English
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Abstract
Learning with reduced labeling standards, such as noisy label, partial label, and supplementary unlabeled data, which we generically refer to as imprecise label, is a commonplace challenge in machine learning tasks. Previous methods tend to propose specific designs for every emerging imprecise label configuration, which is usually unsustainable when multiple configurations of imprecision coexist. In this paper, we introduce imprecise label learning (ILL), a framework for the unification of learning with various imprecise label configurations. ILL leverages expectation-maximization (EM) for modeling the imprecise label information, treating the precise labels as latent variables. Instead of approximating the correct labels for training, it considers the entire distribution of all possible labeling entailed by the imprecise information. We demonstrate that ILL can seamlessly adapt to partial label learning, semi-supervised learning, noisy label learning, and, more importantly, a mixture of these settings, with closed-form learning objectives derived from the unified EM modeling. Notably, ILL surpasses the existing specified techniques for handling imprecise labels, marking the first practical and unified framework with robust and effective performance across various challenging settings. We hope our work will inspire further research on this topic, unleashing the full potential of ILL in wider scenarios where precise labels are expensive and complicated to obtain.
Citation
H. Chen et al., “Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations,” Adv Neural Inf Process Syst, vol. 37, pp. 59621–59654, Dec. 2024.
Source
Advances in Neural Information Processing Systems (NeurIPS 2024)
Conference
Keywords
Imprecise label learning, Expectation-maximization framework, Partial label learning, Semi-supervised learning, Noisy label learning
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Source
Publisher
NEURIPS