Partial label learning (PLL) is an emerging framework in weakly supervised machine
learning with broad application prospects. It handles the case in which each training …

Partial label learning (PLL) is an important problem that allows each training example to be
labeled with a coarse candidate set, which well suits many real-world data annotation …

Partial label learning (PLL), which refers to the classification task where each training
instance is ambiguously annotated with a set of candidate labels, has been recently studied …

Partial-label learning (PLL) is a typical weakly supervised learning problem, where each
training instance is equipped with a set of candidate labels among which only one is the true …

Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …

Partial label learning (PLL) is a typical weakly supervised learning problem, where each
training example is associated with a set of candidate labels among which only one is true …

Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous
candidate labels containing the unknown true label is accessible, contrastive learning has …

We address the problem of partially-labeled multiclass classification, where instead of a
single label per instance, the algorithm is given a candidate set of labels, only one of which …

As an important branch of weakly supervised learning, partial label learning deals with data
where each instance is assigned with a set of candidate labels, whereas only one of them is …

In partial label learning, each training example is associated with a set of candidate labels,
among which only one is valid. An intuitive strategy to learn from partial label examples is to …