Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
This work addresses a common challenge in machine learning where obtaining precise labels is costly or complex, offering a unified solution that is incremental by building on expectation-maximization techniques.
The paper tackles the problem of learning with various imprecise label configurations, such as noisy, partial, or multiple labels, by introducing a unified framework called imprecise label learning (ILL) that uses expectation-maximization to model label distributions, and it demonstrates robust performance across mixed settings, surpassing existing specialized methods.
Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as \textit{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 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. 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.