Learning With Noisy Labels On Cifar 100N

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

Model Name	Accuracy (mean)	Paper Title	Repository
Peer Loss	57.59	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
ILL	65.84	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
F-div	57.10	When Optimizing $f$-divergence is Robust with Label Noise
PGDF	74.08	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
T-Revision	51.55	Are Anchor Points Really Indispensable in Label-Noise Learning?
VolMinNet	57.80	Provably End-to-end Label-Noise Learning without Anchor Points
CE	55.50	-	-
Backward-T	57.14	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
Co-Teaching	60.37	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Positive-LS	55.84	Does label smoothing mitigate label noise?	-
CAL	61.73	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
ELR+	66.72	Early-Learning Regularization Prevents Memorization of Noisy Labels
Divide-Mix	71.13	DivideMix: Learning with Noisy Labels as Semi-supervised Learning
Negative-LS	58.59	To Smooth or Not? When Label Smoothing Meets Noisy Labels
CORES*	55.72	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ELR	58.94	Early-Learning Regularization Prevents Memorization of Noisy Labels
Forward-T	57.01	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
JoCoR	59.97	Combating noisy labels by agreement: A joint training method with co-regularization
CORES	61.15	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
GCE	56.73	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

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