Learning With Noisy Labels On Cifar 10N

Métriques

Accuracy (mean)

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	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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