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.Comment: 29 pages, 3 figures, 16 tables, preprin