Imagen de Portada

Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Jeong, Hyeonsu, Chung, Hye Won
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Label Distribution Shift-Aware Prediction Refinement for Test-Time Adaptation
por: Jang, Minguk, et al.
Publicado: (2024)

Rethinking Dataset Distillation: Hard Truths about Soft Labels
por: Dey, Priyam, et al.
Publicado: (2026)

Refining Labeling Functions with Limited Labeled Data
por: Li, Chenjie, et al.
Publicado: (2025)

Geometric-Averaged Preference Optimization for Soft Preference Labels
por: Furuta, Hiroki, et al.
Publicado: (2024)

Partial Label Clustering
por: Xie, Yutong, et al.
Publicado: (2025)

Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification
por: Dong, Shi, et al.
Publicado: (2024)

Consistently Informative Soft-Label Temperature for Knowledge Distillation
por: Luong, Hoang-Chau, et al.
Publicado: (2026)

Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation
por: Azuma, Kitsuya, et al.
Publicado: (2025)

Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning
por: Tang, Wei, et al.
Publicado: (2024)

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

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

Soft Label PU Learning
por: Zhao, Puning, et al.
Publicado: (2024)

Generating the Ground Truth: Synthetic Data for Soft Label and Label Noise Research
por: de Vries, Sjoerd, et al.
Publicado: (2023)

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

Mitigating Instance-Dependent Label Noise: Integrating Self-Supervised Pretraining with Pseudo-Label Refinement
por: Bala, Gouranga, et al.
Publicado: (2024)

SelMatch: Effectively Scaling Up Dataset Distillation via Selection-Based Initialization and Partial Updates by Trajectory Matching
por: Lee, Yongmin, et al.
Publicado: (2024)

Probabilistic Label Spreading: Efficient and Consistent Estimation of Soft Labels with Epistemic Uncertainty on Graphs
por: Klees, Jonathan, et al.
Publicado: (2026)

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

Nested Graph Pseudo-Label Refinement for Noisy Label Domain Adaptation Learning
por: Wang, Yingxu, et al.
Publicado: (2025)

Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
por: Lingao, Xiao, et al.
Publicado: (2026)

The Bridge-Garden Dilemma in LLM Distillation: Why Mixing Hard and Soft Labels Works
por: Wang, Guanghui, et al.
Publicado: (2026)

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

Rethinking Prompting Strategies for Multi-Label Recognition with Partial Annotations
por: Rawlekar, Samyak, et al.
Publicado: (2024)

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

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

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

Learnability with Partial Labels and Adaptive Nearest Neighbors
por: Errandonea, Nicolas A., et al.
Publicado: (2026)

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

Multi-Label Knowledge Distillation
por: Yang, Penghui, et al.
Publicado: (2023)

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

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

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

A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
por: Mandal, Saptarshi, et al.
Publicado: (2024)

Soft-Label Training Preserves Epistemic Uncertainty
por: Singh, Agamdeep, et al.
Publicado: (2025)

Label Cluster Chains for Multi-Label Classification
por: Gatto, Elaine Cecília, et al.
Publicado: (2024)

Dual-Label Learning With Irregularly Present Labels
por: Li, Mingqian, et al.
Publicado: (2024)

Holistic Optimal Label Selection for Robust Prompt Learning under Partial Labels
por: Zhao, Yaqi, et al.
Publicado: (2026)

Data-Driven Estimation of the False Positive Rate of the Bayes Binary Classifier via Soft Labels
por: Jeong, Minoh, et al.
Publicado: (2024)

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
por: Yang, Yuxuan, et al.
Publicado: (2025)

Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation
por: Zhao, Shiji, et al.
Publicado: (2023)