Papers Partial Label Learning
“Partial Label Learning” 태그가 달린 논문 91편 · 필터 해제
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 LearningTuning the Right Foundation Models is What you Need for Partial Label Learning
Partial label learning (PLL) seeks to train generalizable classifiers from datasets with inexact supervision, a common challenge in real-world applications. Existing studies have developed numerous approaches to progress…
Model SelectionPartial Label LearningExploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
Diminishing the impact of false-positive labels is critical for conducting disambiguation in partial label learning. However, the existing disambiguation strategies mainly focus on exploiting the characteristics of indiv…
AttributePartial Label LearningPartial Label Clustering
Partial label learning (PLL) is a significant weakly supervised learning framework, where each training example corresponds to a set of candidate labels and only one label is the ground-truth label. For the first time, t…
ClusteringConstrained ClusteringGraph LearningPartial Label Learning+1Neuro-symbolic Weak Supervision: Theory and Semantics
Weak supervision allows machine learning models to learn from limited or noisy labels, but it introduces challenges in interpretability and reliability - particularly in multi-instance partial label learning (MI-PLL), wh…
Inductive logic programmingPartial Label LearningRobust Partial-Label Learning by Leveraging Class Activation Values
Real-world training data is often noisy; for example, human annotators assign conflicting class labels to the same instances. Partial-label learning (PLL) is a weakly supervised learning paradigm that allows training cla…
Partial Label LearningWeakly-supervised LearningRealistic Evaluation of Deep Partial-Label Learning Algorithms
Partial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label. In recent years, many deep PLL algorithms have been …
Model SelectionPartial Label LearningWeakly-supervised LearningPartial-Label Learning with Conformal Candidate Cleaning
Real-world data is often ambiguous; for example, human annotation produces instances with multiple conflicting class labels. Partial-label learning (PLL) aims at training a classifier in this challenging setting, where e…
Conformal PredictionPartial Label LearningTowards Robust Incremental Learning under Ambiguous Supervision
Traditional Incremental Learning (IL) targets to handle sequential fully-supervised learning problems where novel classes emerge from time to time. However, due to inherent annotation uncertainty and ambiguity, collectin…
Incremental LearningPartial Label LearningWeakly-supervised LearningMulti-Instance Partial-Label Learning with Margin Adjustment
Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overl…
Partial Label LearningGBRIP: Granular Ball Representation for Imbalanced Partial Label Learning
Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo-labeling from inter-class features, oft…
Partial Label LearningMixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
In partial label learning (PLL), every sample is associated with a candidate label set comprising the ground-truth label and several noisy labels. The conventional PLL assumes the noisy labels are randomly generated (ins…
Partial Label LearningTraining a Label-Noise-Resistant GNN with Reduced Complexity
Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by label noise, that is, a small amount of incorr…
Graph Neural NetworkNode ClassificationPartial Label LearningReduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
Instance-dependent Partial Label Learning (ID-PLL) aims to learn a multi-class predictive model given training instances annotated with candidate labels related to features, among which correct labels are hidden fixed bu…
Partial Label LearningPseudo LabelAn Unbiased Risk Estimator for Partial Label Learning with Augmented Classes
Partial Label Learning (PLL) is a typical weakly supervised learning task, which assumes each training instance is annotated with a set of candidate labels containing the ground-truth label. Recent PLL methods adopt iden…
Partial Label LearningWeakly-supervised Learning