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), which refers to the classification task where each training
instance is ambiguously annotated with a set of candidate labels, has been recently studied …

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 example is associated with a set of candidate labels among which only one is 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 (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 refers to the issue that each training sample corresponds to a
candidate label set containing only one valid label. Feature selection can be viewed as an …

Multi-label learning (MLL) learns from the examples each associated with multiple labels
simultaneously, where the high cost of annotating all relevant labels for 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 …

Vision-language models, such as contrastive language-image pre-training (CLIP), have
demonstrated impressive results in natural image domains. However, these models often …