Learning With Noisy Labels On Cifar 10N

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Accuracy (mean)

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Accuracy (mean)	Paper Title	Repository
SOP+	95.61	Robust Training under Label Noise by Over-parameterization	-
ILL	95.47	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations	-
CORES*	95.25	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	-
CE	87.77	-	-
CAL	91.97	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels	-
Forward-T	88.24	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	-
PGDF	96.11	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels	-
Co-Teaching	91.20	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels	-
ELR+	94.83	Early-Learning Regularization Prevents Memorization of Noisy Labels	-
GNL	92.57	Partial Label Supervision for Agnostic Generative Noisy Label Learning	-
F-div	91.64	When Optimizing $f$-divergence is Robust with Label Noise	-
ProMix	97.39	ProMix: Combating Label Noise via Maximizing Clean Sample Utility	-
ELR	92.38	Early-Learning Regularization Prevents Memorization of Noisy Labels	-
Backward-T	88.13	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	-
Peer Loss	90.75	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates	-
Positive-LS	91.57	Does label smoothing mitigate label noise?	-
GCE	87.85	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels	-
T-Revision	88.52	Are Anchor Points Really Indispensable in Label-Noise Learning?	-
JoCoR	91.44	Combating noisy labels by agreement: A joint training method with co-regularization	-
CORES	91.23	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach	-

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