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Tackling Instance-Dependent Label Noise via a Universal Probabilistic
  Model

Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model

14 January 2021
Qizhou Wang
Bo Han
Tongliang Liu
Gang Niu
Jian Yang
Chen Gong
    NoLa
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Papers citing "Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model"

5 / 5 papers shown
Title
Dynamics-Aware Loss for Learning with Label Noise
Dynamics-Aware Loss for Learning with Label Noise
Xiu-Chuan Li
Xiaobo Xia
Fei Zhu
Tongliang Liu
Xu-Yao Zhang
Cheng-Lin Liu
NoLa
AI4CE
35
6
0
21 Mar 2023
Multi-class Label Noise Learning via Loss Decomposition and Centroid
  Estimation
Multi-class Label Noise Learning via Loss Decomposition and Centroid Estimation
Yongliang Ding
Tao Zhou
Chuang Zhang
Yijing Luo
Juan Tang
Chen Gong
NoLa
32
4
0
21 Mar 2022
Robustness of Accuracy Metric and its Inspirations in Learning with
  Noisy Labels
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
Pengfei Chen
Junjie Ye
Guangyong Chen
Jingwei Zhao
Pheng-Ann Heng
NoLa
103
34
0
08 Dec 2020
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Confidence Scores Make Instance-dependent Label-noise Learning Possible
Antonin Berthon
Bo Han
Gang Niu
Tongliang Liu
Masashi Sugiyama
NoLa
37
104
0
11 Jan 2020
Learning from Binary Labels with Instance-Dependent Corruption
Learning from Binary Labels with Instance-Dependent Corruption
A. Menon
Brendan van Rooyen
Nagarajan Natarajan
NoLa
31
41
0
03 May 2016
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