Learning With Noisy Labels On Cifar 10N 3

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

Model Name	Accuracy (mean)	Paper Title	Repository
GCE	87.58	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels	-
F-div	89.55	When Optimizing $f$-divergence is Robust with Label Noise	-
CAL	90.74	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels	-
Backward-T	86.86	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	-
CORES	89.79	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	-
JoCoR	90.11	Combating noisy labels by agreement: A joint training method with co-regularization	-
Positive-LS	89.82	Does label smoothing mitigate label noise?	-
ELR	91.41	Early-Learning Regularization Prevents Memorization of Noisy Labels	-
VolMinNet	88.19	Provably End-to-end Label-Noise Learning without Anchor Points	-
SOP+	95.39	Robust Training under Label Noise by Over-parameterization	-
Co-Teaching	90.15	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels	-
Co-Teaching+	89.54	How does Disagreement Help Generalization against Label Corruption?	-
Forward-T	87.04	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	-
PSSCL	96.49	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
GNL	91.83	Partial Label Supervision for Agnostic Generative Noisy Label Learning	-
CORES*	94.74	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	-
ILL	95.13	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations	-
CE	85.16	-	-
Negative-LS	90.13	To Smooth or Not? When Label Smoothing Meets Noisy Labels	-
Peer Loss	88.57	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates	-

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