Learning With Noisy Labels On Cifar 100N

평가 지표

Accuracy (mean)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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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