Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
Fuente:
arXiv
Saved in:
| Main Authors: | Tian, Yuxin, Yang, Mouxing, Zhou, Yuhao, Wang, Jian, Ye, Qing, Liu, Tongliang, Niu, Gang, Lv, Jiancheng |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning
by: Ma, Mingchuan, et al.
Published: (2025)
by: Ma, Mingchuan, et al.
Published: (2025)
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
by: Tian, Yuxin, et al.
Published: (2024)
by: Tian, Yuxin, et al.
Published: (2024)
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
by: Zhou, Yuhao, et al.
Published: (2026)
by: Zhou, Yuhao, et al.
Published: (2026)
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
by: Jiang, Xuefeng, et al.
Published: (2025)
by: Jiang, Xuefeng, et al.
Published: (2025)
Style Quantization for Data-Efficient GAN Training
by: Wang, Jian, et al.
Published: (2025)
by: Wang, Jian, et al.
Published: (2025)
Combating Noisy Labels through Fostering Self- and Neighbor-Consistency
by: Sun, Zeren, et al.
Published: (2026)
by: Sun, Zeren, et al.
Published: (2026)
Federated Learning with Instance-Dependent Noisy Label
by: Wang, Lei, et al.
Published: (2023)
by: Wang, Lei, et al.
Published: (2023)
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
by: Li, Jichang, et al.
Published: (2023)
by: Li, Jichang, et al.
Published: (2023)
PLReMix: Combating Noisy Labels with Pseudo-Label Relaxed Contrastive Representation Learning
by: Liu, Xiaoyu, et al.
Published: (2024)
by: Liu, Xiaoyu, et al.
Published: (2024)
Global-Local Medical SAM Adaptor Based on Full Adaption
by: Wang, Meng, et al.
Published: (2024)
by: Wang, Meng, et al.
Published: (2024)
Few-Shot Segmentation with Global and Local Contrastive Learning
by: Liu, Weide, et al.
Published: (2021)
by: Liu, Weide, et al.
Published: (2021)
Learning Normals of Noisy Points by Local Gradient-Aware Surface Filtering
by: Li, Qing, et al.
Published: (2025)
by: Li, Qing, et al.
Published: (2025)
Recovering Global Data Distribution Locally in Federated Learning
by: Yao, Ziyu
Published: (2024)
by: Yao, Ziyu
Published: (2024)
Joint Asymmetric Loss for Learning with Noisy Labels
by: Wang, Jialiang, et al.
Published: (2025)
by: Wang, Jialiang, et al.
Published: (2025)
FedeCouple: Fine-Grained Balancing of Global-Generalization and Local-Adaptability in Federated Learning
by: Yang, Ming, et al.
Published: (2025)
by: Yang, Ming, et al.
Published: (2025)
Learning to Generate Diverse Pedestrian Movements from Web Videos with Noisy Labels
by: Liu, Zhizheng, et al.
Published: (2024)
by: Liu, Zhizheng, et al.
Published: (2024)
Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
by: Zhao, Rui, et al.
Published: (2024)
by: Zhao, Rui, et al.
Published: (2024)
FedGSCA: Medical Federated Learning with Global Sample Selector and Client Adaptive Adjuster under Label Noise
by: Ye, Mengwen, et al.
Published: (2025)
by: Ye, Mengwen, et al.
Published: (2025)
Learning with Noisy Labels: Interconnection of Two Expectation-Maximizations
by: Kim, Heewon, et al.
Published: (2024)
by: Kim, Heewon, et al.
Published: (2024)
Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning
by: Ma, Yanbiao, et al.
Published: (2025)
by: Ma, Yanbiao, et al.
Published: (2025)
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
by: Zhang, Jingfeng, et al.
Published: (2023)
by: Zhang, Jingfeng, et al.
Published: (2023)
Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition
by: Gao, Yuefang, et al.
Published: (2024)
by: Gao, Yuefang, et al.
Published: (2024)
Robust Self-Training with Closed-loop Label Correction for Learning from Noisy Labels
by: Lin, Zhanhui, et al.
Published: (2026)
by: Lin, Zhanhui, et al.
Published: (2026)
Federated Learning Client Pruning for Noisy Labels
by: Morafah, Mahdi, et al.
Published: (2024)
by: Morafah, Mahdi, et al.
Published: (2024)
From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
by: Gabetni, Firas, et al.
Published: (2026)
by: Gabetni, Firas, et al.
Published: (2026)
Leveraging Learning Bias for Noisy Anomaly Detection
by: Zhang, Yuxin, et al.
Published: (2025)
by: Zhang, Yuxin, et al.
Published: (2025)
Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew
by: Peng, Can, et al.
Published: (2025)
by: Peng, Can, et al.
Published: (2025)
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
by: Quan, Jiajie, et al.
Published: (2025)
by: Quan, Jiajie, et al.
Published: (2025)
Collaborative Learning of Scattering and Deep Features for SAR Target Recognition with Noisy Labels
by: Fu, Yimin, et al.
Published: (2025)
by: Fu, Yimin, et al.
Published: (2025)
NICE FACT: Diagnosing and Calibrating VLMs in Quantitative Reasoning for Kinematic Physics
by: Lan, Jian, et al.
Published: (2026)
by: Lan, Jian, et al.
Published: (2026)
Efficiency Follows Global-Local Decoupling
by: Yang, Zhenyu, et al.
Published: (2026)
by: Yang, Zhenyu, et al.
Published: (2026)
Hybrid Local-Global Context Learning for Neural Video Compression
by: Zhai, Yongqi, et al.
Published: (2024)
by: Zhai, Yongqi, et al.
Published: (2024)
Foster Adaptivity and Balance in Learning with Noisy Labels
by: Sheng, Mengmeng, et al.
Published: (2024)
by: Sheng, Mengmeng, et al.
Published: (2024)
Global and Local Entailment Learning for Natural World Imagery
by: Sastry, Srikumar, et al.
Published: (2025)
by: Sastry, Srikumar, et al.
Published: (2025)
EndoOmni: Zero-Shot Cross-Dataset Depth Estimation in Endoscopy by Robust Self-Learning from Noisy Labels
by: Tian, Qingyao, et al.
Published: (2024)
by: Tian, Qingyao, et al.
Published: (2024)
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
by: Hu, Chuanyang, et al.
Published: (2023)
by: Hu, Chuanyang, et al.
Published: (2023)
CARE: Class-Adaptive Expert Consensus for Reliable Learning with Long-Tailed Noisy Labels
by: Li, Mengke, et al.
Published: (2026)
by: Li, Mengke, et al.
Published: (2026)
Robust Noisy Label Learning via Two-Stream Sample Distillation
by: Bai, Sihan, et al.
Published: (2024)
by: Bai, Sihan, et al.
Published: (2024)
Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels
by: Zhang, Tongxu
Published: (2026)
by: Zhang, Tongxu
Published: (2026)
Source-free Video Domain Adaptation by Learning from Noisy Labels
by: Dasgupta, Avijit, et al.
Published: (2023)
by: Dasgupta, Avijit, et al.
Published: (2023)
Similar Items
-
GPS: Distilling Compact Memories via Grid-based Patch Sampling for Efficient Online Class-Incremental Learning
by: Ma, Mingchuan, et al.
Published: (2025) -
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model
by: Tian, Yuxin, et al.
Published: (2024) -
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
by: Zhou, Yuhao, et al.
Published: (2026) -
FNBench: Benchmarking Robust Federated Learning against Noisy Labels
by: Jiang, Xuefeng, et al.
Published: (2025) -
Style Quantization for Data-Efficient GAN Training
by: Wang, Jian, et al.
Published: (2025)