Learning With Noisy Labels On Cifar 10N 2

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

Model Name	Accuracy (mean)	Paper Title	Repository
GNL	91.42	Partial Label Supervision for Agnostic Generative Noisy Label Learning
Co-Teaching	90.30	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Peer Loss	88.76	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
GCE	87.70	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
CORES*	94.88	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ELR+	94.20	Early-Learning Regularization Prevents Memorization of Noisy Labels
Backward-T	86.28	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
T-Revision	87.71	Are Anchor Points Really Indispensable in Label-Noise Learning?
JoCoR	90.21	Combating noisy labels by agreement: A joint training method with co-regularization
SOP	95.31	Robust Training under Label Noise by Over-parameterization
CORES	89.91	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
Forward-T	86.14	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
ELR	91.61	Early-Learning Regularization Prevents Memorization of Noisy Labels
PSSCL	96.21	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
Divide-Mix	90.90	DivideMix: Learning with Noisy Labels as Semi-supervised Learning
CE	86.46	-	-
CAL	90.75	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Positive-LS	89.35	Does label smoothing mitigate label noise?	-
Negative-LS	90.37	To Smooth or Not? When Label Smoothing Meets Noisy Labels
ILL	95.04	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

0 of 23 row(s) selected.