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Robust Learning by Self-Transition for Handling Noisy Labels

Robust Learning by Self-Transition for Handling Noisy Labels

8 December 2020
Hwanjun Song
Minseok Kim
Dongmin Park
Yooju Shin
Jae-Gil Lee
    NoLa
ArXivPDFHTML

Papers citing "Robust Learning by Self-Transition for Handling Noisy Labels"

34 / 34 papers shown
Title
Diffusion on Graph: Augmentation of Graph Structure for Node Classification
Diffusion on Graph: Augmentation of Graph Structure for Node Classification
Yancheng Wang
Changyu Liu
Yingzhen Yang
DiffM
GNN
138
0
0
16 Mar 2025
Early Stopping Against Label Noise Without Validation Data
Early Stopping Against Label Noise Without Validation Data
Suqin Yuan
Lei Feng
Tongliang Liu
NoLa
155
18
0
11 Feb 2025
Learning from Noisy Labels with Deep Neural Networks: A Survey
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song
Minseok Kim
Dongmin Park
Yooju Shin
Jae-Gil Lee
NoLa
80
976
0
16 Jul 2020
Early-Learning Regularization Prevents Memorization of Noisy Labels
Early-Learning Regularization Prevents Memorization of Noisy Labels
Sheng Liu
Jonathan Niles-Weed
N. Razavian
C. Fernandez‐Granda
NoLa
51
561
0
30 Jun 2020
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Junnan Li
R. Socher
Guosheng Lin
NoLa
59
1,021
0
18 Feb 2020
How does Early Stopping Help Generalization against Label Noise?
How does Early Stopping Help Generalization against Label Noise?
Hwanjun Song
Minseok Kim
Dongmin Park
Jae-Gil Lee
NoLa
28
75
0
19 Nov 2019
Consistency Regularization for Generative Adversarial Networks
Consistency Regularization for Generative Adversarial Networks
Han Zhang
Zizhao Zhang
Augustus Odena
Honglak Lee
GAN
52
284
0
26 Oct 2019
Product Image Recognition with Guidance Learning and Noisy Supervision
Product Image Recognition with Guidance Learning and Noisy Supervision
Qing Li
Xiaojiang Peng
Liangliang Cao
Wenbin Du
Hao Xing
Yu Qiao
NoLa
14
21
0
26 Jul 2019
Understanding and Utilizing Deep Neural Networks Trained with Noisy
  Labels
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen
B. Liao
Guangyong Chen
Shengyu Zhang
NoLa
36
383
0
13 May 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning
MixMatch: A Holistic Approach to Semi-Supervised Learning
David Berthelot
Nicholas Carlini
Ian Goodfellow
Nicolas Papernot
Avital Oliver
Colin Raffel
114
3,009
0
06 May 2019
Unsupervised Label Noise Modeling and Loss Correction
Unsupervised Label Noise Modeling and Loss Correction
Eric Arazo Sanchez
Diego Ortego
Paul Albert
Noel E. O'Connor
Kevin McGuinness
NoLa
55
609
0
25 Apr 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise
  for Overparameterized Neural Networks
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks
Mingchen Li
Mahdi Soltanolkotabi
Samet Oymak
NoLa
91
352
0
27 Mar 2019
Identity Crisis: Memorization and Generalization under Extreme
  Overparameterization
Identity Crisis: Memorization and Generalization under Extreme Overparameterization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Michael C. Mozer
Y. Singer
24
89
0
13 Feb 2019
How does Disagreement Help Generalization against Label Corruption?
How does Disagreement Help Generalization against Label Corruption?
Xingrui Yu
Bo Han
Jiangchao Yao
Gang Niu
Ivor W. Tsang
Masashi Sugiyama
NoLa
29
778
0
14 Jan 2019
An Empirical Study of Example Forgetting during Deep Neural Network
  Learning
An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva
Alessandro Sordoni
Rémi Tachet des Combes
Adam Trischler
Yoshua Bengio
Geoffrey J. Gordon
84
723
0
12 Dec 2018
Dimensionality-Driven Learning with Noisy Labels
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma
Yisen Wang
Michael E. Houle
Shuo Zhou
S. Erfani
Shutao Xia
S. Wijewickrema
James Bailey
NoLa
52
429
0
07 Jun 2018
Co-teaching: Robust Training of Deep Neural Networks with Extremely
  Noisy Labels
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han
Quanming Yao
Xingrui Yu
Gang Niu
Miao Xu
Weihua Hu
Ivor Tsang
Masashi Sugiyama
NoLa
73
2,051
0
18 Apr 2018
Iterative Learning with Open-set Noisy Labels
Iterative Learning with Open-set Noisy Labels
Yisen Wang
Weiyang Liu
Xingjun Ma
James Bailey
H. Zha
Le Song
Shutao Xia
NoLa
49
326
0
31 Mar 2018
Learning to Reweight Examples for Robust Deep Learning
Learning to Reweight Examples for Robust Deep Learning
Mengye Ren
Wenyuan Zeng
Binh Yang
R. Urtasun
OOD
NoLa
107
1,419
0
24 Mar 2018
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
  on Corrupted Labels
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang
Zhengyuan Zhou
Thomas Leung
Li Li
Li Fei-Fei
NoLa
83
1,448
0
14 Dec 2017
CleanNet: Transfer Learning for Scalable Image Classifier Training with
  Label Noise
CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
Kuang-Huei Lee
Xiaodong He
Lei Zhang
Linjun Yang
NoLa
48
454
0
20 Nov 2017
WebVision Database: Visual Learning and Understanding from Web Data
WebVision Database: Visual Learning and Understanding from Web Data
Wen Li
Limin Wang
Wei Li
E. Agustsson
Luc Van Gool
VLM
57
435
0
09 Aug 2017
A Closer Look at Memorization in Deep Networks
A Closer Look at Memorization in Deep Networks
Devansh Arpit
Stanislaw Jastrzebski
Nicolas Ballas
David M. Krueger
Emmanuel Bengio
...
Tegan Maharaj
Asja Fischer
Aaron Courville
Yoshua Bengio
Simon Lacoste-Julien
TDI
88
1,801
0
16 Jun 2017
Decoupling "when to update" from "how to update"
Decoupling "when to update" from "how to update"
Eran Malach
Shai Shalev-Shwartz
NoLa
53
564
0
08 Jun 2017
Active Bias: Training More Accurate Neural Networks by Emphasizing High
  Variance Samples
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang
Erik Learned-Miller
Andrew McCallum
57
349
0
24 Apr 2017
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
293
1,275
0
06 Mar 2017
Attend in groups: a weakly-supervised deep learning framework for
  learning from web data
Attend in groups: a weakly-supervised deep learning framework for learning from web data
Bohan Zhuang
Lingqiao Liu
Yao Li
Chunhua Shen
Ian Reid
NoLa
33
89
0
30 Nov 2016
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
238
4,612
0
10 Nov 2016
Temporal Ensembling for Semi-Supervised Learning
Temporal Ensembling for Semi-Supervised Learning
S. Laine
Timo Aila
UQCV
152
2,543
0
07 Oct 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction
  Approach
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Giorgio Patrini
A. Rozza
A. Menon
Richard Nock
Zhuang Li
NoLa
75
1,444
0
13 Sep 2016
Inception-v4, Inception-ResNet and the Impact of Residual Connections on
  Learning
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy
Sergey Ioffe
Vincent Vanhoucke
Alexander A. Alemi
258
14,196
0
23 Feb 2016
Distilling the Knowledge in a Neural Network
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton
Oriol Vinyals
J. Dean
FedML
108
19,448
0
09 Mar 2015
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott E. Reed
Honglak Lee
Dragomir Anguelov
Christian Szegedy
D. Erhan
Andrew Rabinovich
NoLa
91
1,014
0
20 Dec 2014
Stochastic Gradient Descent for Non-smooth Optimization: Convergence
  Results and Optimal Averaging Schemes
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir
Tong Zhang
132
573
0
08 Dec 2012
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