Tackling Noisy Labels with Network Parameter Additive Decomposition
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Jingyi, Xia, Xiaobo, Lan, Long, Wu, Xinghao, Yu, Jun, Yang, Wenjing, Han, Bo, Liu, Tongliang |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Label Noise on Graph via Topological Sample Selection
by: Wu, Yuhao, et al.
Published: (2024)
by: Wu, Yuhao, et al.
Published: (2024)
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024)
by: Jiang, Xuefeng, et al.
Published: (2024)
Noisy Test-Time Adaptation in Vision-Language Models
by: Cao, Chentao, et al.
Published: (2025)
by: Cao, Chentao, et al.
Published: (2025)
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
by: Yuan, Suqin, et al.
Published: (2025)
by: Yuan, Suqin, et al.
Published: (2025)
Robust Training of Federated Models with Extremely Label Deficiency
by: Zhang, Yonggang, et al.
Published: (2024)
by: Zhang, Yonggang, et al.
Published: (2024)
Unveiling Causal Reasoning in Large Language Models: Reality or Mirage?
by: Chi, Haoang, et al.
Published: (2025)
by: Chi, Haoang, et al.
Published: (2025)
Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition
by: Azad, Tarhib Al, et al.
Published: (2025)
by: Azad, Tarhib Al, et al.
Published: (2025)
Few-Shot Adversarial Prompt Learning on Vision-Language Models
by: Zhou, Yiwei, et al.
Published: (2024)
by: Zhou, Yiwei, et al.
Published: (2024)
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
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)
Understanding Robust Overfitting from the Feature Generalization Perspective
by: Yu, Chaojian, et al.
Published: (2023)
by: Yu, Chaojian, et al.
Published: (2023)
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
by: Chen, Ling-Hao, et al.
Published: (2023)
by: Chen, Ling-Hao, et al.
Published: (2023)
Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
by: Tian, Yuxin, et al.
Published: (2024)
by: Tian, Yuxin, et al.
Published: (2024)
Refined Coreset Selection: Towards Minimal Coreset Size under Model Performance Constraints
by: Xia, Xiaobo, et al.
Published: (2023)
by: Xia, Xiaobo, et al.
Published: (2023)
The Exploration of Error Bounds in Classification with Noisy Labels
by: Liu, Haixia, et al.
Published: (2025)
by: Liu, Haixia, et al.
Published: (2025)
HiDe-PET: Continual Learning via Hierarchical Decomposition of Parameter-Efficient Tuning
by: Wang, Liyuan, et al.
Published: (2024)
by: Wang, Liyuan, et al.
Published: (2024)
Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
Omnimodal Dataset Distillation via High-order Proxy Alignment
by: Gao, Yuxuan, et al.
Published: (2026)
by: Gao, Yuxuan, et al.
Published: (2026)
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
by: Lin, Runqi, et al.
Published: (2023)
by: Lin, Runqi, et al.
Published: (2023)
Resultant: Incremental Effectiveness on Likelihood for Unsupervised Out-of-Distribution Detection
by: Li, Yewen, et al.
Published: (2024)
by: Li, Yewen, et al.
Published: (2024)
Early Stopping Against Label Noise Without Validation Data
by: Yuan, Suqin, et al.
Published: (2025)
by: Yuan, Suqin, et al.
Published: (2025)
Understanding and Mitigating the Bias in Sample Selection for Learning with Noisy Labels
by: Wei, Qi, et al.
Published: (2024)
by: Wei, Qi, et al.
Published: (2024)
Online Multi-Label Classification under Noisy and Changing Label Distribution
by: Zou, Yizhang, et al.
Published: (2024)
by: Zou, Yizhang, et al.
Published: (2024)
FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning
by: Ali, Abdulmoneam, et al.
Published: (2026)
by: Ali, Abdulmoneam, et al.
Published: (2026)
Noisy Early Stopping for Noisy Labels
by: Toner, William, et al.
Published: (2024)
by: Toner, William, 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)
Tackling Data Heterogeneity in Federated Learning via Loss Decomposition
by: Zeng, Shuang, et al.
Published: (2024)
by: Zeng, Shuang, et al.
Published: (2024)
When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
by: Xia, Riting, et al.
Published: (2025)
by: Xia, Riting, et al.
Published: (2025)
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
by: Lin, Runqi, et al.
Published: (2024)
by: Lin, Runqi, et al.
Published: (2024)
Bayesian Experimental Design for Model Discrepancy Calibration: An Auto-Differentiable Ensemble Kalman Inversion Approach
by: Yang, Huchen, et al.
Published: (2025)
by: Yang, Huchen, et al.
Published: (2025)
Bayesian Experimental Design for Model Discrepancy Calibration: A Rivalry between Kullback--Leibler Divergence and Wasserstein Distance
by: Yang, Huchen, et al.
Published: (2026)
by: Yang, Huchen, et al.
Published: (2026)
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification
by: Li, Zhuo, et al.
Published: (2024)
by: Li, Zhuo, et al.
Published: (2024)
Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds
by: Li, Shikun, et al.
Published: (2023)
by: Li, Shikun, et al.
Published: (2023)
Learning Discriminative Dynamics with Label Corruption for Noisy Label Detection
by: Kim, Suyeon, et al.
Published: (2024)
by: Kim, Suyeon, et al.
Published: (2024)
Negative Label Guided OOD Detection with Pretrained Vision-Language Models
by: Jiang, Xue, et al.
Published: (2024)
by: Jiang, Xue, et al.
Published: (2024)
F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel
by: Shen, Yutong, et al.
Published: (2026)
by: Shen, Yutong, et al.
Published: (2026)
Potential Energy based Mixture Model for Noisy Label Learning
by: Wang, Zijia, et al.
Published: (2024)
by: Wang, Zijia, et al.
Published: (2024)
Reinforcement Learning with Verifiable yet Noisy Rewards under Imperfect Verifiers
by: Cai, Xin-Qiang, et al.
Published: (2025)
by: Cai, Xin-Qiang, et al.
Published: (2025)
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
by: Wang, Qizhou, et al.
Published: (2024)
by: Wang, Qizhou, et al.
Published: (2024)
What If the Input is Expanded in OOD Detection?
by: Zhang, Boxuan, et al.
Published: (2024)
by: Zhang, Boxuan, et al.
Published: (2024)
Similar Items
-
Mitigating Label Noise on Graph via Topological Sample Selection
by: Wu, Yuhao, et al.
Published: (2024) -
Tackling Noisy Clients in Federated Learning with End-to-end Label Correction
by: Jiang, Xuefeng, et al.
Published: (2024) -
Noisy Test-Time Adaptation in Vision-Language Models
by: Cao, Chentao, et al.
Published: (2025) -
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
by: Yuan, Suqin, et al.
Published: (2025) -
Robust Training of Federated Models with Extremely Label Deficiency
by: Zhang, Yonggang, et al.
Published: (2024)