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 …

Recently, learning with soft labels has been shown to achieve better performance than
learning with hard labels in terms of model generalization, calibration, and robustness …

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 …

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 …

To reduce the difficulty of annotation, partial label learning (PLL) has been widely studied,
where each example is ambiguously annotated with a set of candidate labels instead of the …

In partial-label learning (PLL), each training example has a set of candidate labels, among
which only one is the true label. Most existing PLL studies focus on the instance …

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 …

Since the advent of knowledge distillation, many researchers have been intrigued by the
$\textit {dark knowledge} $ hidden in the soft labels generated by the teacher model. This …

Learning with reduced labeling standards, such as noisy label, partial label, and multiple
label candidates, which we generically refer to as\textit {imprecise} labels, is a commonplace …

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