Copertina

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tang, Wei, Zhang, Weijia, Zhang, Min-Ling
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
di: Tang, Wei, et al.
Pubblicazione: (2025)

Multi-Instance Partial-Label Learning with Margin Adjustment
di: Tang, Wei, et al.
Pubblicazione: (2025)

Tuning the Right Foundation Models is What you Need for Partial Label Learning
di: He, Kuang, et al.
Pubblicazione: (2025)

Realistic Evaluation of Deep Partial-Label Learning Algorithms
di: Wang, Wei, et al.
Pubblicazione: (2025)

Exploiting the Potential Supervision Information of Clean Samples in Partial Label Learning
di: Wang, Guangtai, et al.
Pubblicazione: (2025)

Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
di: Zhao, Rui, et al.
Pubblicazione: (2026)

Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
di: Chen, Yu, et al.
Pubblicazione: (2026)

Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
di: Qiao, Congyu, et al.
Pubblicazione: (2024)

Noise-Resistant Label Reconstruction Feature Selection for Partial Multi-Label Learning
di: Gao, Wanfu, et al.
Pubblicazione: (2025)

Unleashing the Power of Vision-Language Models for Long-Tailed Multi-Label Visual Recognition
di: Tang, Wei, et al.
Pubblicazione: (2025)

Mixed Blessing: Class-Wise Embedding guided Instance-Dependent Partial Label Learning
di: Yang, Fuchao, et al.
Pubblicazione: (2024)

Weakly-Supervised Contrastive Learning for Imprecise Class Labels
di: Zhou, Zi-Hao, et al.
Pubblicazione: (2025)

From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification
di: Liu, Xin, et al.
Pubblicazione: (2024)

Learning Causal Transition Matrix for Instance-dependent Label Noise
di: Li, Jiahui, et al.
Pubblicazione: (2024)

Investigating ECG Diagnosis with Ambiguous Labels using Partial Label Learning
di: Rahmani, Sana, et al.
Pubblicazione: (2025)

Semi-supervised Contrastive Learning Using Partial Label Information
di: Hansen, Colin B., et al.
Pubblicazione: (2020)

Learning from Label Proportions and Covariate-shifted Instances
di: Singh, Sagalpreet, et al.
Pubblicazione: (2024)

Partial Label Clustering
di: Xie, Yutong, et al.
Pubblicazione: (2025)

Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
di: Si, Chongjie, et al.
Pubblicazione: (2025)

CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning
di: Tian, Shiyu, et al.
Pubblicazione: (2023)

Complementary to Multiple Labels: A Correlation-Aware Correction Approach
di: Gao, Yi, et al.
Pubblicazione: (2023)

Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification
di: Yu, Simon, et al.
Pubblicazione: (2023)

LADA: Scalable Label-Specific CLIP Adapter for Continual Learning
di: Luo, Mao-Lin, et al.
Pubblicazione: (2025)

Partial-Label Learning with a Reject Option
di: Fuchs, Tobias, et al.
Pubblicazione: (2024)

Partial-Label Learning with Conformal Candidate Cleaning
di: Fuchs, Tobias, et al.
Pubblicazione: (2025)

Diffusion Disambiguation Models for Partial Label Learning
di: Fan, Jinfu, et al.
Pubblicazione: (2025)

Exploiting Label Skewness for Spiking Neural Networks in Federated Learning
di: Yu, Di, et al.
Pubblicazione: (2024)

Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels
di: Jeong, Hyeonsu, et al.
Pubblicazione: (2024)

Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation
di: Fuchs, Tobias, et al.
Pubblicazione: (2025)

Federated Learning with Instance-Dependent Noisy Label
di: Wang, Lei, et al.
Pubblicazione: (2023)

Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
di: Chen, Yu, et al.
Pubblicazione: (2026)

Scalable Label Distribution Learning for Multi-Label Classification
di: Zhao, Xingyu, et al.
Pubblicazione: (2023)

Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning
di: Si, Chongjie, et al.
Pubblicazione: (2023)

Graph Partial Label Learning with Potential Cause Discovering
di: Gao, Hang, et al.
Pubblicazione: (2024)

Label Distribution Learning from Logical Label
di: Jia, Yuheng, et al.
Pubblicazione: (2023)

Pseudo-labelling meets Label Smoothing for Noisy Partial Label Learning
di: Saravanan, Darshana, et al.
Pubblicazione: (2024)

Instance Relation Learning Network with Label Knowledge Propagation for Few-shot Multi-label Intent Detection
di: Zhao, Shiman, et al.
Pubblicazione: (2025)

Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation
di: Kou, Zhiqiang, et al.
Pubblicazione: (2025)

Multi-Label Bayesian Active Learning with Inter-Label Relationships
di: Qi, Yuanyuan, et al.
Pubblicazione: (2024)

No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Prediction
di: Jia, Qinglin, et al.
Pubblicazione: (2025)