Learning With Noisy Labels On Cifar 10N 3

Métriques

Accuracy (mean)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy (mean)	Paper Title	Repository
GCE	87.58	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
F-div	89.55	When Optimizing $f$-divergence is Robust with Label Noise
CAL	90.74	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Backward-T	86.86	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
CORES	89.79	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
JoCoR	90.11	Combating noisy labels by agreement: A joint training method with co-regularization
Positive-LS	89.82	Does label smoothing mitigate label noise?	-
ELR	91.41	Early-Learning Regularization Prevents Memorization of Noisy Labels
VolMinNet	88.19	Provably End-to-end Label-Noise Learning without Anchor Points
SOP+	95.39	Robust Training under Label Noise by Over-parameterization
Co-Teaching	90.15	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Co-Teaching+	89.54	How does Disagreement Help Generalization against Label Corruption?
Forward-T	87.04	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
PSSCL	96.49	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
GNL	91.83	Partial Label Supervision for Agnostic Generative Noisy Label Learning
CORES*	94.74	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ILL	95.13	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
CE	85.16	-	-
Negative-LS	90.13	To Smooth or Not? When Label Smoothing Meets Noisy Labels
Peer Loss	88.57	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates

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