Image Classification on CIFAR-10N-Worst

Metric: Accuracy (mean) (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy (mean)▼	Extra Data	Paper	Date↕	Code
1	ProMix	96.16	No	ProMix: Combating Label Noise via Maximizing Cle...	2022-07-21	Code
2	PSSCL	95.12	No	-	-	Code
3	PGDF	93.65	No	Sample Prior Guided Robust Model Learning to Sup...	2021-12-02	Code
4	ILL	93.58	No	Imprecise Label Learning: A Unified Framework fo...	2023-05-22	Code
5	SOP+	93.24	No	Robust Training under Label Noise by Over-parame...	2022-02-28	Code
6	Divide-Mix	92.56	No	DivideMix: Learning with Noisy Labels as Semi-su...	2020-02-18	Code
7	CORES*	91.66	No	Learning with Instance-Dependent Label Noise: A ...	2020-10-05	Code
8	ELR+	91.09	No	Early-Learning Regularization Prevents Memorizat...	2020-06-30	Code
9	GNL	86.99	No	Partial Label Supervision for Agnostic Generativ...	2023-08-02	Code
10	CAL	85.36	No	Clusterability as an Alternative to Anchor Point...	2021-02-10	Code
11	Co-Teaching	83.83	No	Co-teaching: Robust Training of Deep Neural Netw...	2018-04-18	Code
12	CORES	83.6	No	Learning with Instance-Dependent Label Noise: A ...	2020-10-05	Code
13	ELR	83.58	No	Early-Learning Regularization Prevents Memorizat...	2020-06-30	Code
14	JoCoR	83.37	No	Combating noisy labels by agreement: A joint tra...	2020-03-05	Code
15	Co-Teaching+	83.26	No	How does Disagreement Help Generalization agains...	2019-01-14	Code
16	Negative-LS	82.99	No	-	-	Code
17	Positive-LS	82.76	No	Does label smoothing mitigate label noise?	2020-03-05	-
18	F-div	82.53	No	When Optimizing $f$-divergence is Robust with La...	2020-11-07	Code
19	Peer Loss	82.53	No	Peer Loss Functions: Learning from Noisy Labels ...	2019-10-08	Code
20	GCE	80.66	No	Generalized Cross Entropy Loss for Training Deep...	2018-05-20	Code
21	VolMinNet	80.53	No	Provably End-to-end Label-Noise Learning without...	2021-02-04	Code
22	T-Revision	80.48	No	Are Anchor Points Really Indispensable in Label-...	2019-06-01	Code
23	Forward-T	79.79	No	Making Deep Neural Networks Robust to Label Nois...	2016-09-13	Code
24	CE	77.69	No	-	-	-
25	Backward-T	77.61	No	Making Deep Neural Networks Robust to Label Nois...	2016-09-13	Code

#1ProMixSOTA
96.16
Accuracy (mean)· 2022-07-21
ProMix: Combating Label Noise via Maximizing Clean Sample Utility Code
#2PSSCL
95.12
Accuracy (mean)
No paperCode
#3PGDFSOTA
93.65
Accuracy (mean)· 2021-12-02
Sample Prior Guided Robust Model Learning to Suppress Noisy Labels Code
#4ILL
93.58
Accuracy (mean)· 2023-05-22
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations Code
#5SOP+
93.24
Accuracy (mean)· 2022-02-28
Robust Training under Label Noise by Over-parameterization Code
#6Divide-MixSOTA
92.56
Accuracy (mean)· 2020-02-18
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Code
#7CORES*
91.66
Accuracy (mean)· 2020-10-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Code
#8ELR+
91.09
Accuracy (mean)· 2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels Code
#9GNL
86.99
Accuracy (mean)· 2023-08-02
Partial Label Supervision for Agnostic Generative Noisy Label Learning Code
#10CAL
85.36
Accuracy (mean)· 2021-02-10
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels Code
#11Co-TeachingSOTA
83.83
Accuracy (mean)· 2018-04-18
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Code
#12CORES
83.6
Accuracy (mean)· 2020-10-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Code
#13ELR
83.58
Accuracy (mean)· 2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels Code
#14JoCoR
83.37
Accuracy (mean)· 2020-03-05
Combating noisy labels by agreement: A joint training method with co-regularization Code
#15Co-Teaching+
83.26
Accuracy (mean)· 2019-01-14
How does Disagreement Help Generalization against Label Corruption?Code
#16Negative-LS
82.99
Accuracy (mean)
No paperCode
#17Positive-LS
82.76
Accuracy (mean)· 2020-03-05
Does label smoothing mitigate label noise?
#18F-div
82.53
Accuracy (mean)· 2020-11-07
When Optimizing $f$-divergence is Robust with Label Noise Code
#19Peer Loss
82.53
Accuracy (mean)· 2019-10-08
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates Code
#20GCE
80.66
Accuracy (mean)· 2018-05-20
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Code
#21VolMinNet
80.53
Accuracy (mean)· 2021-02-04
Provably End-to-end Label-Noise Learning without Anchor Points Code
#22T-Revision
80.48
Accuracy (mean)· 2019-06-01
Are Anchor Points Really Indispensable in Label-Noise Learning?Code
#23Forward-TSOTA
79.79
Accuracy (mean)· 2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Code
#24CE
77.69
Accuracy (mean)
No paper
#25Backward-T
77.61
Accuracy (mean)· 2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Code