Cover Image

Federated Learning with Instance-Dependent Noisy Label

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Lei, Bian, Jieming, Xu, Jie
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
by: Bian, Jieming, et al.
Published: (2024)

Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
by: Wang, Lei, et al.
Published: (2024)

MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
by: Hu, Chuanyang, et al.
Published: (2023)

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

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
by: Tian, Yuxin, et al.
Published: (2024)

Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
by: Zhao, Rui, et al.
Published: (2024)

Foster Adaptivity and Balance in Learning with Noisy Labels
by: Sheng, Mengmeng, et al.
Published: (2024)

Joint Asymmetric Loss for Learning with Noisy Labels
by: Wang, Jialiang, et al.
Published: (2025)

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

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

Probabilistic Machine Learning for Noisy Labels in Earth Observation
by: Kondylatos, Spyros, et al.
Published: (2025)

Learning from Noisy Labels with Contrastive Co-Transformer
by: Han, Yan, et al.
Published: (2025)

Pairwise Similarity Distribution Clustering for Noisy Label Learning
by: Bai, Sihan
Published: (2024)

Federated Learning Client Pruning for Noisy Labels
by: Morafah, Mahdi, et al.
Published: (2024)

ChronoSelect: Robust Learning with Noisy Labels via Dynamics Temporal Memory
by: Wang, Jianchao, et al.
Published: (2025)

Radial-Angular Geometry for Reliable Update Diagnosis in Noisy-Label Learning
by: Peng, Ningkang, et al.
Published: (2026)

Learning Disease State from Noisy Ordinal Disease Progression Labels
by: Schmidt, Gustav, et al.
Published: (2025)

Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation
by: Liu, Fengbei, et al.
Published: (2023)

NCSAM Noise-Compensated Sharpness-Aware Minimization for Noisy Label Learning
by: Xu, Jiayu, et al.
Published: (2026)

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
by: Bae, HeeSun, et al.
Published: (2024)

Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning
by: Wan, Wenhai, et al.
Published: (2023)

FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology
by: Peng, Yuanzhe, et al.
Published: (2024)

Combating Noisy Labels via Dynamic Connection Masking
by: Zhang, Xinlei, et al.
Published: (2025)

Vision-Language Models are Strong Noisy Label Detectors
by: Wei, Tong, et al.
Published: (2024)

Exploring Vacant Classes in Label-Skewed Federated Learning
by: Guo, Kuangpu, et al.
Published: (2024)

Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning
by: Wang, Hanxuan, et al.
Published: (2025)

Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels
by: Lu, Yangdi, et al.
Published: (2024)

When Accuracy Is Not Enough: Uncertainty Collapse between Noisy Label Learning and Out-of-Distribution Detection
by: Peng, Ningkang, et al.
Published: (2026)

Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels
by: Wang, Ke, et al.
Published: (2023)

Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios
by: Xu, Jie, et al.
Published: (2023)

Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
by: Lin, Zhanhui, et al.
Published: (2026)

Learning with Instance-Dependent Noisy Labels by Anchor Hallucination and Hard Sample Label Correction
by: Huang, Po-Hsuan, et al.
Published: (2024)

Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods
by: Hu, Jiamian, et al.
Published: (2024)

Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space
by: Pan, Linchao, et al.
Published: (2025)

May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels
by: Millunzi, Monica, et al.
Published: (2024)

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

When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections
by: Chelouche, Keryan, et al.
Published: (2024)

BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
by: Zhang, Jingfeng, et al.
Published: (2023)

Federated Learning with Label-Masking Distillation
by: Lu, Jianghu, et al.
Published: (2024)

Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labels
by: Wang, Kening, et al.
Published: (2026)