Partial Label Learning
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Benchmarks
Most implemented
Exploiting Class Activation Value for Partial-Label Learning
Partial Label Clustering
Multi-Instance Partial-Label Learning with Margin Adjustment
Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
Training a Label-Noise-Resistant GNN with Reduced Complexity
Papers
PaSta: Noisy Node Classification with Partial Label Learning
Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. Howe…
Partial Label LearningNode ClassificationMitigating Instance Entanglement in Instance-Dependent Partial Label Learning
Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influen…
Weakly Supervised ClassificationPartial Label LearningCombating Noisy Labels through Fostering Self- and Neighbor-Consistency
Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning. Deep networks are vulnerable to such label-corrupted samples due to the memorization effect. One major stream of pr…
Partial Label LearningInvestigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning
Label ambiguity is an inherent and largely unaddressed challenge in real-world electrocardiogram (ECG) diagnosis, arising from overlapping conditions and diagnostic disagreements. However, current ECG models are trained …
Partial Label LearningPartial Label Learning for Automated Theorem Proving
We formulate learning guided Automated Theorem Proving as Partial Label Learning, building the first bridge across these fields of research and providing a theoretical framework for dealing with alternative proofs during…
Automated Theorem ProvingPartial Label LearningDiffusion Disambiguation Models for Partial Label Learning
Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of \emph{partial label learning} (PLL) is to identify the ground-truth label from a set of candidate label…
Partial Label Learning