Image Classification on CIFAR-10N-Random1

Metric: Accuracy (mean) (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy (mean)▼	Extra Data	Paper	Date↕	Code
1	ProMix	96.97	No	ProMix: Combating Label Noise via Maximizing Cle...	2022-07-21	Code
2	PSSCL	96.17	No	-	-	Code
3	PGDF	96.01	No	Sample Prior Guided Robust Model Learning to Sup...	2021-12-02	Code
4	SOP+	95.28	No	Robust Training under Label Noise by Over-parame...	2022-02-28	Code
5	ILL	94.85	No	Imprecise Label Learning: A Unified Framework fo...	2023-05-22	Code
6	CORES*	94.45	No	Learning with Instance-Dependent Label Noise: A ...	2020-10-05	Code
7	ELR+	94.43	No	Early-Learning Regularization Prevents Memorizat...	2020-06-30	Code
8	GNL	91.97	No	Partial Label Supervision for Agnostic Generativ...	2023-08-02	Code
9	ELR	91.46	No	Early-Learning Regularization Prevents Memorizat...	2020-06-30	Code
10	CAL	90.93	No	Clusterability as an Alternative to Anchor Point...	2021-02-10	Code
11	Co-Teaching	90.33	No	Co-teaching: Robust Training of Deep Neural Netw...	2018-04-18	Code
12	JoCoR	90.3	No	Combating noisy labels by agreement: A joint tra...	2020-03-05	Code
13	Negative-LS	90.29	No	To Smooth or Not? When Label Smoothing Meets Noi...	2021-06-08	Code
14	Divide-Mix	90.18	No	DivideMix: Learning with Noisy Labels as Semi-su...	2020-02-18	Code
15	Positive-LS	89.8	No	Does label smoothing mitigate label noise?	2020-03-05	-
16	Co-Teaching+	89.7	No	How does Disagreement Help Generalization agains...	2019-01-14	Code
17	F-div	89.7	No	When Optimizing $f$-divergence is Robust with La...	2020-11-07	Code
18	CORES	89.66	No	Learning with Instance-Dependent Label Noise: A ...	2020-10-05	Code
19	Peer Loss	89.06	No	Peer Loss Functions: Learning from Noisy Labels ...	2019-10-08	Code
20	T-Revision	88.33	No	Are Anchor Points Really Indispensable in Label-...	2019-06-01	Code
21	VolMinNet	88.3	No	Provably End-to-end Label-Noise Learning without...	2021-02-04	Code
22	GCE	87.61	No	Generalized Cross Entropy Loss for Training Deep...	2018-05-20	Code
23	Backward-T	87.14	No	Making Deep Neural Networks Robust to Label Nois...	2016-09-13	Code
24	Forward-T	86.88	No	Making Deep Neural Networks Robust to Label Nois...	2016-09-13	Code

#1ProMixSOTA
96.97
Accuracy (mean)· 2022-07-21
ProMix: Combating Label Noise via Maximizing Clean Sample Utility Code
#2PSSCL
96.17
Accuracy (mean)
No paperCode
#3PGDFSOTA
96.01
Accuracy (mean)· 2021-12-02
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels Code
#4SOP+
95.28
Accuracy (mean)· 2022-02-28
Robust Training under Label Noise by Over-parameterization Code
#5ILL
94.85
Accuracy (mean)· 2023-05-22
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations Code
#6CORES*SOTA
94.45
Accuracy (mean)· 2020-10-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Code
#7ELR+SOTA
94.43
Accuracy (mean)· 2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels Code
#8GNL
91.97
Accuracy (mean)· 2023-08-02
Partial Label Supervision for Agnostic Generative Noisy Label Learning Code
#9ELR
91.46
Accuracy (mean)· 2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels Code
#10CAL
90.93
Accuracy (mean)· 2021-02-10
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels Code
#11Co-TeachingSOTA
90.33
Accuracy (mean)· 2018-04-18
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Code
#12JoCoR
90.3
Accuracy (mean)· 2020-03-05
Combating noisy labels by agreement: A joint training method with co-regularization Code
#13Negative-LS
90.29
Accuracy (mean)· 2021-06-08
To Smooth or Not? When Label Smoothing Meets Noisy Labels Code
#14Divide-Mix
90.18
Accuracy (mean)· 2020-02-18
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Code
#15Positive-LS
89.8
Accuracy (mean)· 2020-03-05
Does label smoothing mitigate label noise?
#16Co-Teaching+
89.7
Accuracy (mean)· 2019-01-14
How does Disagreement Help Generalization against Label Corruption?Code
#17F-div
89.7
Accuracy (mean)· 2020-11-07
When Optimizing $f$-divergence is Robust with Label Noise Code
#18CORES
89.66
Accuracy (mean)· 2020-10-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Code
#19Peer Loss
89.06
Accuracy (mean)· 2019-10-08
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates Code
#20T-Revision
88.33
Accuracy (mean)· 2019-06-01
Are Anchor Points Really Indispensable in Label-Noise Learning?Code
#21VolMinNet
88.3
Accuracy (mean)· 2021-02-04
Provably End-to-end Label-Noise Learning without Anchor Points Code
#22GCE
87.61
Accuracy (mean)· 2018-05-20
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Code
#23Backward-TSOTA
87.14
Accuracy (mean)· 2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Code
#24Forward-T
86.88
Accuracy (mean)· 2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Code