Papers Partial Label Learning
“Partial Label Learning” 태그가 달린 논문 91편 · 필터 해제
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
Vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated impressive results in natural image domains. However, these models often struggle when applied to specialized domains like…
Contrastive LearningImage-text RetrievalPartial Label LearningPseudo Label+3Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
Learning with reduced labeling standards, such as noisy label, partial label, and multiple label candidates, which we generically refer to as \textit{imprecise} labels, is a commonplace challenge in machine learning task…
Learning with noisy labelsPartial Label LearningA Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unified and correct label via majority voting …
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)NER+1Complementary Classifier Induced Partial Label Learning
In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In d…
Partial Label LearningvalidTowards 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…
Contrastive Learningimage-classificationImage ClassificationPartial Label Learning+2COSST: Multi-organ Segmentation with Partially Labeled Datasets Using Comprehensive Supervisions and Self-training
Deep learning models have demonstrated remarkable success in multi-organ segmentation but typically require large-scale datasets with all organs of interest annotated. However, medical image datasets are often low in sam…
Computed Tomography (CT)Medical Image SegmentationOrgan SegmentationPartial Label Learning+3Adversary-Aware Partial label learning with Label distillation
To ensure that the data collected from human subjects is entrusted with a secret, rival labels are introduced to conceal the information provided by the participants on purpose. The corresponding learning task can be for…
Partial Label LearningCroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
Partial-label learning (PLL) is an important weakly supervised learning problem, which allows each training example to have a candidate label set instead of a single ground-truth label. Identification-based methods have …
Partial Label LearningWeakly-supervised LearningPseudo Labels Regularization for Imbalanced Partial-Label Learning
Partial-label learning (PLL) is an important branch of weakly supervised learning where the single ground truth resides in a set of candidate labels, while the research rarely considers the label imbalance. A recent stud…
Long-tail LearningPartial Label LearningPseudo LabelWeakly-supervised LearningPartial Label Learning for Emotion Recognition from EEG
Fully supervised learning has recently achieved promising performance in various electroencephalography (EEG) learning tasks by training on large datasets with ground truth labels. However, labeling EEG data for affectiv…
EEGElectroencephalogram (EEG)Emotion RecognitionPartial Label Learning+1Unreliable Partial Label Learning with Recursive Separation
Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-trut…
Partial Label LearningWeakly-supervised LearningLong-Tailed Partial Label Learning via Dynamic Rebalancing
Real-world data usually couples the label ambiguity and heavy imbalance, challenging the algorithmic robustness of partial label learning (PLL) and long-tailed learning (LT). The straightforward combination of LT and PLL…
Partial Label LearningALIM: Adjusting Label Importance Mechanism for Noisy Partial Label Learning
Noisy partial label learning (noisy PLL) is an important branch of weakly supervised learning. Unlike PLL where the ground-truth label must conceal in the candidate label set, noisy PLL relaxes this constraint and allows…
Model OptimizationPartial Label LearningWeakly-supervised LearningCandidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning
In partial-label learning (PLL), each training example has a set of candidate labels, among which only one is the true label. Most existing PLL studies focus on the instance-independent (II) case, where the generatio…
Partial Label LearningMulti-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision
Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associ…
Gaussian ProcessesPartial Label LearningLearning with Partial Labels from Semi-supervised Perspective
Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recen…
Contrastive LearningPartial Label LearningvalidIRNet: Iterative Refinement Network for Noisy Partial Label Learning
Partial label learning (PLL) is a typical weakly supervised learning, where each sample is associated with a set of candidate labels. The basic assumption of PLL is that the ground-truth label must reside in the candidat…
Data AugmentationPartial Label LearningWeakly-supervised LearningController-Guided Partial Label Consistency Regularization with Unlabeled Data
Partial label learning (PLL) learns from training examples each associated with multiple candidate labels, among which only one is valid. In recent years, benefiting from the strong capability of dealing with ambiguous s…
Contrastive LearningData AugmentationPartial Label LearningvalidG2NetPL: Generic Game-Theoretic Network for Partial-Label Image Classification
Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, since it could be expensive in practice to annotate all the labels in every …
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Continual Learning with Evolving Class Ontologies
Lifelong learners must recognize concept vocabularies that evolve over time. A common yet underexplored scenario is learning with class labels that continually refine/expand old classes. For example, humans learn to reco…
class-incremental learningClass Incremental LearningContinual Learningimage-classification+4