Late Stopping: Avoiding Confidently Learning from Mislabeled Examples

26 August 2023

Papers citing "Late Stopping: Avoiding Confidently Learning from Mislabeled Examples"

9 / 9 papers shown

Title
Handling Label Noise via Instance-Level Difficulty Modeling and Dynamic Optimization Kuan Zhang Chengliang Chai Jingzhe Xu Chi Zhang Ye Yuan Guoren Wang Lei Cao NoLa 61 0 0 01 May 2025
Early Stopping Against Label Noise Without Validation Data Suqin Yuan Lei Feng Tongliang Liu NoLa 96 14 0 11 Feb 2025
Mislabeled examples detection viewed as probing machine learning models: concepts, survey and extensive benchmark Thomas George Pierre Nodet A. Bondu Vincent Lemaire VLM 32 0 0 21 Oct 2024
Mitigating Label Noise on Graph via Topological Sample Selection Yuhao Wu Jiangchao Yao Xiaobo Xia Jun-chen Yu Ruxing Wang Bo Han Tongliang Liu NoLa 47 2 0 04 Mar 2024
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning Zhuo Huang Li Shen Jun-chen Yu Bo Han Tongliang Liu FedML 21 21 0 25 Oct 2023
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting Runqi Lin Chaojian Yu Bo Han Tongliang Liu 29 7 0 13 Oct 2023
CroSel: Cross Selection of Confident Pseudo Labels for Partial-Label Learning Shiyun Tian Hongxin Wei Yiqun Wang Lei Feng 19 5 0 18 Mar 2023
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 313 497 0 05 Mar 2020
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 55 172 0 24 May 2019