Exabytes of data are generated daily by humans, leading to the growing needs for new
efforts in dealing with the grand challenges for multi-label learning brought by big data. For …

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 peculiar weakly-supervised learning task where the training
samples are generally associated with a set of candidate labels instead of single ground …

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 …

Partial label learning (PLL) learns from a typical weak supervision, where each training
instance is labeled with a set of ambiguous candidate labels (CLs) instead of its exact …

Partial multilabel learning (PML) aims to learn from training data, where each instance is
associated with a set of candidate labels, among which only a part is correct. The common …

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 …

Redundant Label Learning (RLL) aims at inducing a robust model from training data, where
each example is associated with a set of candidate labels, among which some of them are …

Partial label learning (PLL) is an important problem that allows each training example to be
labeled with a coarse candidate set with the ground-truth label included. However, in a more …