Regularly Truncated M-estimators for Learning with Noisy Labels

Regularly Truncated M-estimators for Learning with Noisy Labels

2 September 2023

Xiaobo Xia

Bo Han

Papers citing "Regularly Truncated M-estimators for Learning with Noisy Labels"

8 / 8 papers shown

Title
Early Stopping Against Label Noise Without Validation Data Suqin Yuan Lei Feng Tongliang Liu NoLa 96 14 0 11 Feb 2025
Meta-Learning Guided Label Noise Distillation for Robust Signal Modulation Classification Xiaoyang Hao Zhixi Feng Tongqing Peng Shuyuan Yang NoLa 38 5 0 09 Aug 2024
Can We Treat Noisy Labels as Accurate? Yuxiang Zheng Zhongyi Han Yilong Yin Xin Gao Tongliang Liu 25 1 0 21 May 2024
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance Ling-Hao Chen Yuanshuo Zhang Taohua Huang Liangcai Su Zeyi Lin Xi Xiao Xiaobo Xia Tongliang Liu NoLa 28 9 0 13 Dec 2023
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 116 120 0 04 Feb 2021
Improved Baselines with Momentum Contrastive Learning Xinlei Chen Haoqi Fan Ross B. Girshick Kaiming He SSL 238 3,367 0 09 Mar 2020
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 497 0 05 Mar 2020
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 50 172 0 24 May 2019