Partial-label learning (PLL) is an important weakly supervised learning problem which
allows each training example to have a candidate label set instead of a single ground-truth …

Partial label learning (PLL) is a weakly-supervised learning paradigm where each training
instance is paired with a set of candidate labels (partial label), one of which is the true label …

Learning with inaccurate supervision is often encountered in weakly supervised learning,
and researchers have invested a considerable amount of time and effort in designing …

One major challenge in weakly supervised learning is learning from inexact supervision,
ranging from partial labels (PLs) with* redundant* information to the extreme of unlabeled …