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Robustness of Accuracy Metric and its Inspirations in Learning with
  Noisy Labels

Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

8 December 2020
Pengfei Chen
Junjie Ye
Guangyong Chen
Jingwei Zhao
Pheng-Ann Heng
    NoLa
ArXivPDFHTML

Papers citing "Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels"

36 / 36 papers shown
Title
Early Stopping Against Label Noise Without Validation Data
Early Stopping Against Label Noise Without Validation Data
Suqin Yuan
Lei Feng
Tongliang Liu
NoLa
241
19
0
11 Feb 2025
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels
Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels
Erjian Guo
Zicheng Wang
Zhen Zhao
Luping Zhou
NoLa
125
0
0
12 Jan 2025
Error-Bounded Correction of Noisy Labels
Error-Bounded Correction of Noisy Labels
Songzhu Zheng
Pengxiang Wu
A. Goswami
Mayank Goswami
Dimitris N. Metaxas
Chao Chen
NoLa
53
119
0
19 Nov 2020
Normalized Loss Functions for Deep Learning with Noisy Labels
Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma
Hanxun Huang
Yisen Wang
Simone Romano
S. Erfani
James Bailey
NoLa
65
441
0
24 Jun 2020
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Part-dependent Label Noise: Towards Instance-dependent Label Noise
Xiaobo Xia
Tongliang Liu
Bo Han
Nannan Wang
Biwei Huang
Haifeng Liu
Gang Niu
Dacheng Tao
Masashi Sugiyama
NoLa
44
67
0
14 Jun 2020
Rethinking Importance Weighting for Deep Learning under Distribution
  Shift
Rethinking Importance Weighting for Deep Learning under Distribution Shift
Tongtong Fang
Nan Lu
Gang Niu
Masashi Sugiyama
50
139
0
08 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
105
1,029
0
18 Feb 2020
SELF: Learning to Filter Noisy Labels with Self-Ensembling
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Philipp Kratzer
Marc Toussaint
Thi Phuong Nhung Ngo
T. Nguyen
Jim Mainprice
Thomas Brox
NoLa
83
315
0
04 Oct 2019
On the Efficacy of Knowledge Distillation
On the Efficacy of Knowledge Distillation
Ligang He
Rui Mao
92
609
0
03 Oct 2019
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
Pengfei Chen
B. Liao
Guangyong Chen
Shengyu Zhang
NoLa
41
8
0
13 Jun 2019
Are Anchor Points Really Indispensable in Label-Noise Learning?
Are Anchor Points Really Indispensable in Label-Noise Learning?
Xiaobo Xia
Tongliang Liu
N. Wang
Bo Han
Chen Gong
Gang Niu
Masashi Sugiyama
NoLa
68
378
0
01 Jun 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
65
386
0
13 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
78
611
0
25 Apr 2019
On Symmetric Losses for Learning from Corrupted Labels
On Symmetric Losses for Learning from Corrupted Labels
Nontawat Charoenphakdee
Jongyeong Lee
Masashi Sugiyama
NoLa
54
105
0
27 Jan 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
58
782
0
14 Jan 2019
Masking: A New Perspective of Noisy Supervision
Masking: A New Perspective of Noisy Supervision
Bo Han
Jiangchao Yao
Gang Niu
Mingyuan Zhou
Ivor Tsang
Ya Zhang
Masashi Sugiyama
NoLa
68
255
0
21 May 2018
Generalized Cross Entropy Loss for Training Deep Neural Networks with
  Noisy Labels
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhilu Zhang
M. Sabuncu
NoLa
78
2,602
0
20 May 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
110
2,066
0
18 Apr 2018
Joint Optimization Framework for Learning with Noisy Labels
Joint Optimization Framework for Learning with Noisy Labels
Daiki Tanaka
Daiki Ikami
T. Yamasaki
Kiyoharu Aizawa
NoLa
72
710
0
30 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
141
1,424
0
24 Mar 2018
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe
  Noise
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks
Mantas Mazeika
Duncan Wilson
Kevin Gimpel
NoLa
127
554
0
14 Feb 2018
Robust Loss Functions under Label Noise for Deep Neural Networks
Robust Loss Functions under Label Noise for Deep Neural Networks
Aritra Ghosh
Himanshu Kumar
P. Sastry
NoLa
OOD
64
956
0
27 Dec 2017
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
95
1,451
0
14 Dec 2017
Learning From Noisy Singly-labeled Data
Learning From Noisy Singly-labeled Data
A. Khetan
Zachary Chase Lipton
Anima Anandkumar
NoLa
49
162
0
13 Dec 2017
Improved Regularization of Convolutional Neural Networks with Cutout
Improved Regularization of Convolutional Neural Networks with Cutout
Terrance Devries
Graham W. Taylor
109
3,764
0
15 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
120
1,816
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
75
566
0
08 Jun 2017
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
336
4,625
0
10 Nov 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
90
1,452
0
13 Sep 2016
Wide Residual Networks
Wide Residual Networks
Sergey Zagoruyko
N. Komodakis
332
7,980
0
23 May 2016
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
193,814
0
10 Dec 2015
Distilling the Knowledge in a Neural Network
Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton
Oriol Vinyals
J. Dean
FedML
340
19,634
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
111
1,021
0
20 Dec 2014
Optimal Learners for Multiclass Problems
Optimal Learners for Multiclass Problems
Amit Daniely
Shai Shalev-Shwartz
58
86
0
10 May 2014
Making Risk Minimization Tolerant to Label Noise
Making Risk Minimization Tolerant to Label Noise
Aritra Ghosh
Naresh Manwani
P. Sastry
NoLa
140
214
0
14 Mar 2014
Noise Tolerance under Risk Minimization
Noise Tolerance under Risk Minimization
Naresh Manwani
S. M. I. P. S. Sastry
NoLa
174
276
0
24 Sep 2011
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