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 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 instance is equipped with a set of candidate labels among which only one is the true …

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 …

Partial label learning aims to induce a multi-class classifier from training examples where
each of them is associated with a set of candidate labels, among which only one is the …

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 …

Learning-based approaches to graph matching have been developed and explored for
more than a decade, have grown rapidly in scope and popularity in recent years. However …

Partial Multi-Label learning (PML) learns from the ambiguous data where each instance is
associated with a candidate label set, where only a part is correct. The key to solve such …

Abstract Partial Multi-Label Learning (PML) aims to learn from the training data where each
instance is associated with a set of candidate labels, among which only a part of them are …

As an emerging weakly supervised learning framework, partial label learning considers
inaccurate supervision where each training example is associated with multiple candidate …