Partial label learning (PLL) is a weakly supervised methodology dealing with tasks that have
annotation problems by replacing the single label with a collection of candidate labels …

Partial-label learning (PLL) is a weakly supervised learning problem in which each example
is associated with multiple candidate labels and only one is the true label. In recent years …

Instance-dependent Partial Label Learning (ID-PLL) aims to learn a multi-class predictive
model given training instances annotated with candidate labels related to features, among …

In partial label learning (PLL), every sample is associated with a candidate label set
comprising the ground-truth label and several noisy labels. The conventional PLL assumes …

Multi-instance partial-label learning (MIPL) is a paradigm where each training example is
encapsulated as a multiinstance bag associated with the candidate label set, which includes …

Partial label learning (PLL) addresses situations where each training example is associated
with a set of candidate labels, among which only one corresponds to the true class label. As …