Learning from Noisy Labels with Coarse-to-Fine Sample Credibility
Modeling

Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling

23 August 2022

Jinlong Peng

Yabiao Wang

Papers citing "Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling"

5 / 5 papers shown

Title
Learning from Noisy Labels with Decoupled Meta Label Purifier Yuanpeng Tu Boshen Zhang Yuxi Li Liang Liu Jian Li Yabiao Wang Chengjie Wang C. Zhao NoLa 26 27 0 14 Feb 2023
L2B: Learning to Bootstrap Robust Models for Combating Label Noise Yuyin Zhou Xianhang Li Fengze Liu Qingyue Wei Xuxi Chen Lequan Yu Cihang Xie M. Lungren Lei Xing NoLa 32 3 0 09 Feb 2022
Provably End-to-end Label-Noise Learning without Anchor Points Xuefeng Li Tongliang Liu Bo Han Gang Niu Masashi Sugiyama NoLa 112 120 0 04 Feb 2021
Combating noisy labels by agreement: A joint training method with co-regularization Hongxin Wei Lei Feng Xiangyu Chen Bo An NoLa 303 494 0 05 Mar 2020
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 47 172 0 24 May 2019