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 …

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

Real-world data usually couples the label ambiguity and heavy imbalance, challenging the
algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The …

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each
training sample is represented as a multi-instance bag associated with a candidate label set …

We consider a weakly supervised learning scenario where the supervision signal is
generated by a transition function $\sigma $ of labels associated with multiple input …

Partial label learning (PLL) aims to train multiclass classifiers from the examples each
annotated with a set of candidate labels where a fixed but unknown candidate label is …

In many real-world tasks, the concerned objects can be represented as a multi-instance bag
associated with a candidate label set, which consists of one ground-truth label and several …