Learning With Noisy Labels On Cifar 100N

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

		Paper Title	Repository
PGDF	74.08	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
ProMix	73.39	ProMix: Combating Label Noise via Maximizing Clean Sample Utility
PSSCL	72.00	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels	-
Divide-Mix	71.13	DivideMix: Learning with Noisy Labels as Semi-supervised Learning
SOP+	67.81	Robust Training under Label Noise by Over-parameterization
ELR+	66.72	Early-Learning Regularization Prevents Memorization of Noisy Labels
ILL	65.84	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
CAL	61.73	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
CORES	61.15	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Co-Teaching	60.37	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
JoCoR	59.97	Combating noisy labels by agreement: A joint training method with co-regularization
ELR	58.94	Early-Learning Regularization Prevents Memorization of Noisy Labels
Negative-LS	58.59	To Smooth or Not? When Label Smoothing Meets Noisy Labels
Co-Teaching+	57.88	How does Disagreement Help Generalization against Label Corruption?
VolMinNet	57.80	Provably End-to-end Label-Noise Learning without Anchor Points
Peer Loss	57.59	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
Backward-T	57.14	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
F-div	57.10	When Optimizing $f$-divergence is Robust with Label Noise
Forward-T	57.01	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
GCE	56.73	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

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Learning With Noisy Labels On Cifar 100N

Metrics

Results