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 …

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 …

Abstract Complementary Label Learning (CLL), a crucial aspect of weakly supervised
learning, has seen significant theoretical and practical advancements. However, a …

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 …

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 …

Abstract Partial Multi-Label Learning (PML) is a more practical learning paradigm, in which
the labeling information is ambiguated. Most existing PML algorithms rely on assumptions to …

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 …

Partial Label Learning (PLL) is a type of weakly supervised learning where each training
instance is assigned a set of candidate labels, but only one label is the ground-truth …

Abstract Machine unlearning aims to remove specific knowledge from a well-trained
machine learning model. This topic has gained significant attention recently due to the …