Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

Hao Chen, Ankit Shah, Jindong Wang, Ran Tao, Yidong Wang, Xing Xie, Masashi Sugiyama, Rita Singh, Bhiksha Raj

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: Imprecise Label Learning; Partial Label Learning; Noisy Label Learning; Semi-Supervised Learning; Expectation-Maximization

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Abstract:

Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as imprecise labels, 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 towards 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. Notably, ILL surpasses the existing specified techniques for handling imprecise labels, marking the first 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.

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Submission Number: 3986