Learning With Noisy Labels On Cifar 10N 3

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

		Paper Title
PSSCL	96.49	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
SOP+	95.39	Robust Training under Label Noise by Over-parameterization
ILL	95.13	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
CORES*	94.74	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ELR+	94.34	Early-Learning Regularization Prevents Memorization of Noisy Labels
GNL	91.83	Partial Label Supervision for Agnostic Generative Noisy Label Learning
ELR	91.41	Early-Learning Regularization Prevents Memorization of Noisy Labels
CAL	90.74	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Co-Teaching	90.15	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Negative-LS	90.13	To Smooth or Not? When Label Smoothing Meets Noisy Labels
JoCoR	90.11	Combating noisy labels by agreement: A joint training method with co-regularization
Divide-Mix	89.97	DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Positive-LS	89.82	Does label smoothing mitigate label noise?
CORES	89.79	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
F-div	89.55	When Optimizing $f$-divergence is Robust with Label Noise
Co-Teaching+	89.54	How does Disagreement Help Generalization against Label Corruption?
Peer Loss	88.57	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
VolMinNet	88.19	Provably End-to-end Label-Noise Learning without Anchor Points
T-Revision	87.79	Are Anchor Points Really Indispensable in Label-Noise Learning?
GCE	87.58	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

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Learning With Noisy Labels On Cifar 10N 3

Metrics

Results