Document Text Classification on CIFAR-10N-Random3

Metric: Accuracy (mean) (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy (mean)▼	Extra Data	Paper	Date↕	Code
1	PSSCL	96.49	No	-	-	Code
2	SOP+	95.39	No	Robust Training under Label Noise by Over-parame...	2022-02-28	Code
3	ILL	95.13	No	Imprecise Label Learning: A Unified Framework fo...	2023-05-22	Code
4	CORES*	94.74	No	Learning with Instance-Dependent Label Noise: A ...	2020-10-05	Code
5	ELR+	94.34	No	Early-Learning Regularization Prevents Memorizat...	2020-06-30	Code
6	GNL	91.83	No	Partial Label Supervision for Agnostic Generativ...	2023-08-02	Code
7	ELR	91.41	No	Early-Learning Regularization Prevents Memorizat...	2020-06-30	Code
8	CAL	90.74	No	Clusterability as an Alternative to Anchor Point...	2021-02-10	Code
9	Co-Teaching	90.15	No	Co-teaching: Robust Training of Deep Neural Netw...	2018-04-18	Code
10	Negative-LS	90.13	No	To Smooth or Not? When Label Smoothing Meets Noi...	2021-06-08	Code
11	JoCoR	90.11	No	Combating noisy labels by agreement: A joint tra...	2020-03-05	Code
12	Divide-Mix	89.97	No	DivideMix: Learning with Noisy Labels as Semi-su...	2020-02-18	Code
13	Positive-LS	89.82	No	Does label smoothing mitigate label noise?	2020-03-05	-
14	CORES	89.79	No	Learning with Instance-Dependent Label Noise: A ...	2020-10-05	Code
15	F-div	89.55	No	When Optimizing $f$-divergence is Robust with La...	2020-11-07	Code
16	Co-Teaching+	89.54	No	How does Disagreement Help Generalization agains...	2019-01-14	Code
17	Peer Loss	88.57	No	Peer Loss Functions: Learning from Noisy Labels ...	2019-10-08	Code
18	VolMinNet	88.19	No	Provably End-to-end Label-Noise Learning without...	2021-02-04	Code
19	T-Revision	87.79	No	Are Anchor Points Really Indispensable in Label-...	2019-06-01	Code
20	GCE	87.58	No	Generalized Cross Entropy Loss for Training Deep...	2018-05-20	Code
21	Forward-T	87.04	No	Making Deep Neural Networks Robust to Label Nois...	2016-09-13	Code
22	Backward-T	86.86	No	Making Deep Neural Networks Robust to Label Nois...	2016-09-13	Code
23	CE	85.16	No	-	-	-

#1PSSCL
96.49
Accuracy (mean)
No paperCode
#2SOP+SOTA
95.39
Accuracy (mean)· 2022-02-28
Robust Training under Label Noise by Over-parameterization Code
#3ILL
95.13
Accuracy (mean)· 2023-05-22
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations Code
#4CORES*SOTA
94.74
Accuracy (mean)· 2020-10-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Code
#5ELR+SOTA
94.34
Accuracy (mean)· 2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels Code
#6GNL
91.83
Accuracy (mean)· 2023-08-02
Partial Label Supervision for Agnostic Generative Noisy Label Learning Code
#7ELR
91.41
Accuracy (mean)· 2020-06-30
Early-Learning Regularization Prevents Memorization of Noisy Labels Code
#8CAL
90.74
Accuracy (mean)· 2021-02-10
Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels Code
#9Co-TeachingSOTA
90.15
Accuracy (mean)· 2018-04-18
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels Code
#10Negative-LS
90.13
Accuracy (mean)· 2021-06-08
To Smooth or Not? When Label Smoothing Meets Noisy Labels Code
#11JoCoR
90.11
Accuracy (mean)· 2020-03-05
Combating noisy labels by agreement: A joint training method with co-regularization Code
#12Divide-Mix
89.97
Accuracy (mean)· 2020-02-18
DivideMix: Learning with Noisy Labels as Semi-supervised Learning Code
#13Positive-LS
89.82
Accuracy (mean)· 2020-03-05
Does label smoothing mitigate label noise?
#14CORES
89.79
Accuracy (mean)· 2020-10-05
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach Code
#15F-div
89.55
Accuracy (mean)· 2020-11-07
When Optimizing $f$-divergence is Robust with Label Noise Code
#16Co-Teaching+
89.54
Accuracy (mean)· 2019-01-14
How does Disagreement Help Generalization against Label Corruption?Code
#17Peer Loss
88.57
Accuracy (mean)· 2019-10-08
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates Code
#18VolMinNet
88.19
Accuracy (mean)· 2021-02-04
Provably End-to-end Label-Noise Learning without Anchor Points Code
#19T-Revision
87.79
Accuracy (mean)· 2019-06-01
Are Anchor Points Really Indispensable in Label-Noise Learning?Code
#20GCE
87.58
Accuracy (mean)· 2018-05-20
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels Code
#21Forward-TSOTA
87.04
Accuracy (mean)· 2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Code
#22Backward-T
86.86
Accuracy (mean)· 2016-09-13
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Code
#23CE
85.16
Accuracy (mean)
No paper