Learning With Noisy Labels On Cifar 10N 1

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Accuracy (mean)

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Accuracy (mean)	Paper Title	Repository
Negative-LS	90.29	To Smooth or Not? When Label Smoothing Meets Noisy Labels	-
F-div	89.70	When Optimizing $f$-divergence is Robust with Label Noise	-
VolMinNet	88.30	Provably End-to-end Label-Noise Learning without Anchor Points	-
JoCoR	90.30	Combating noisy labels by agreement: A joint training method with co-regularization	-
GNL	91.97	Partial Label Supervision for Agnostic Generative Noisy Label Learning	-
T-Revision	88.33	Are Anchor Points Really Indispensable in Label-Noise Learning?	-
Backward-T	87.14	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	-
Forward-T	86.88	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	-
PGDF	96.01	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels	-
Co-Teaching+	89.70	How does Disagreement Help Generalization against Label Corruption?	-
Peer Loss	89.06	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates	-
PSSCL	96.17	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
CORES*	94.45	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	-
Co-Teaching	90.33	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels	-
ELR	91.46	Early-Learning Regularization Prevents Memorization of Noisy Labels	-
ELR+	94.43	Early-Learning Regularization Prevents Memorization of Noisy Labels	-
GCE	87.61	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels	-
Divide-Mix	90.18	DivideMix: Learning with Noisy Labels as Semi-supervised Learning	-
ILL	94.85	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations	-
Positive-LS	89.80	Does label smoothing mitigate label noise?	-

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