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Consistency Regularization Can Improve Robustness to Label Noise

Consistency Regularization Can Improve Robustness to Label Noise

4 October 2021
Erik Englesson
Hossein Azizpour
    NoLa
ArXivPDFHTML

Papers citing "Consistency Regularization Can Improve Robustness to Label Noise"

3 / 3 papers shown
Title
Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking
Beyond Self-Consistency: Loss-Balanced Perturbation-Based Regularization Improves Industrial-Scale Ads Ranking
Ilqar Ramazanli
Hamid Eghbalzadeh
Xiaoyi Liu
Yang Wang
Jiaxiang Fu
Kaushik Rangadurai
Sem Park
Bo Long
Xue Feng
46
0
0
05 Feb 2025
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
32
69
0
08 Jun 2021
Mean teachers are better role models: Weight-averaged consistency
  targets improve semi-supervised deep learning results
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Antti Tarvainen
Harri Valpola
OOD
MoMe
261
1,275
0
06 Mar 2017
1