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Found 8 papers, 3 papers with code
Towards Effective Visual Representations for Partial-Label Learning
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 recently boosted the performance of PLL on vision tasks, attributed to representations learned by contrasting the same/different classes of entities.
Progressive Purification for Instance-Dependent Partial Label Learning
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 correct.
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
To cope with the challenge, we investigate single-positive multi-label learning (SPMLL) where each example is annotated with only one relevant label, and show that one can successfully learn a theoretically grounded multi-label classifier for the problem.
Learning with Proper Partial Labels
Recently, various approaches on partial-label learning have been proposed under different generation models of candidate label sets.
On the Robustness of Average Losses for Partial-Label Learning
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL).
Compact Learning for Multi-Label Classification
In this paper, we present a compact learning (CL) framework to embed the features and labels simultaneously and with mutual guidance.
Provably Consistent Partial-Label Learning
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels.
Progressive Identification of True Labels for Partial-Label Learning
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label.
