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Understanding Instance-Level Label Noise: Disparate Impacts and
  Treatments
v1v2 (latest)

Understanding Instance-Level Label Noise: Disparate Impacts and Treatments

10 February 2021
Yang Liu
    NoLa
ArXiv (abs)PDFHTML

Papers citing "Understanding Instance-Level Label Noise: Disparate Impacts and Treatments"

14 / 14 papers shown
Title
Label Smoothing is a Pragmatic Information Bottleneck
Label Smoothing is a Pragmatic Information Bottleneck
Sota Kudo
24
0
0
12 Aug 2025
Regretful Decisions under Label Noise
Regretful Decisions under Label Noise
Sujay Nagaraj
Yang Liu
Flavio du Pin Calmon
Berk Ustun
NoLa
211
3
0
12 Apr 2025
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
Muthuraman Chidambaram
Rong Ge
AAML
162
2
0
10 Feb 2024
Instance-Dependent Noisy Label Learning via Graphical Modelling
Instance-Dependent Noisy Label Learning via Graphical Modelling
Arpit Garg
Cuong C. Nguyen
Rafael Felix
Thanh-Toan Do
G. Carneiro
NoLa
117
32
0
02 Sep 2022
To Aggregate or Not? Learning with Separate Noisy Labels
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei
Zhaowei Zhu
Tianyi Luo
Ehsan Amid
Abhishek Kumar
Yang Liu
NoLa
102
43
0
14 Jun 2022
Identifiability of Label Noise Transition Matrix
Identifiability of Label Noise Transition Matrix
Yang Liu
Hao Cheng
Kun Zhang
NoLa
148
51
0
04 Feb 2022
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with
  Lower-Quality Features
Beyond Images: Label Noise Transition Matrix Estimation for Tasks with Lower-Quality Features
Zhaowei Zhu
Jialu Wang
Yang Liu
NoLa
140
39
0
02 Feb 2022
Detecting Corrupted Labels Without Training a Model to Predict
Detecting Corrupted Labels Without Training a Model to Predict
Zhaowei Zhu
Zihao Dong
Yang Liu
NoLa
311
75
0
12 Oct 2021
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Zhaowei Zhu
Tianyi Luo
Yang Liu
337
40
0
12 Oct 2021
Can Less be More? When Increasing-to-Balancing Label Noise Rates
  Considered Beneficial
Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial
Yang Liu
Jialu Wang
NoLa
121
20
0
13 Jul 2021
To Smooth or Not? When Label Smoothing Meets Noisy Labels
To Smooth or Not? When Label Smoothing Meets Noisy Labels
Jiaheng Wei
Hangyu Liu
Tongliang Liu
Gang Niu
Masashi Sugiyama
Yang Liu
NoLa
244
83
0
08 Jun 2021
Policy Learning Using Weak Supervision
Policy Learning Using Weak Supervision
Jingkang Wang
Hongyi Guo
Zhaowei Zhu
Yang Liu
OffRL
122
16
0
05 Oct 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via
  Influence Estimation
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation
Vitaly Feldman
Chiyuan Zhang
TDI
285
514
0
09 Aug 2020
Combating noisy labels by agreement: A joint training method with
  co-regularization
Combating noisy labels by agreement: A joint training method with co-regularization
Jianguo Huang
Lei Feng
Xiangyu Chen
Bo An
NoLa
572
552
0
05 Mar 2020
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