Learning With Noisy Labels On Cifar 10N Worst

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

Model Name	Accuracy (mean)	Paper Title	Repository
Co-Teaching	83.83	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
ELR	83.58	Early-Learning Regularization Prevents Memorization of Noisy Labels
GNL	86.99	Partial Label Supervision for Agnostic Generative Noisy Label Learning
T-Revision	80.48	Are Anchor Points Really Indispensable in Label-Noise Learning?
JoCoR	83.37	Combating noisy labels by agreement: A joint training method with co-regularization
PSSCL	95.12	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
PGDF	93.65	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
CE	77.69	-	-
Negative-LS	82.99	Understanding Generalized Label Smoothing when Learning with Noisy Labels	-
Forward-T	79.79	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Co-Teaching+	83.26	How does Disagreement Help Generalization against Label Corruption?
F-div	82.53	When Optimizing $f$-divergence is Robust with Label Noise
ProMix	96.16	ProMix: Combating Label Noise via Maximizing Clean Sample Utility
GCE	80.66	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Divide-Mix	92.56	DivideMix: Learning with Noisy Labels as Semi-supervised Learning
CAL	85.36	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
VolMinNet	80.53	Provably End-to-end Label-Noise Learning without Anchor Points
CORES	83.60	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ELR+	91.09	Early-Learning Regularization Prevents Memorization of Noisy Labels
Peer Loss	82.53	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates

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