Learning With Noisy Labels On Cifar 10N Worst

Metrics

Accuracy (mean)

Results

Performance results of various models on this benchmark

		Paper Title
ProMix	96.16	ProMix: Combating Label Noise via Maximizing Clean Sample Utility
PSSCL	95.12	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels
PGDF	93.65	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
ILL	93.58	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations
SOP+	93.24	Robust Training under Label Noise by Over-parameterization
Divide-Mix	92.56	DivideMix: Learning with Noisy Labels as Semi-supervised Learning
CORES*	91.66	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ELR+	91.09	Early-Learning Regularization Prevents Memorization of Noisy Labels
GNL	86.99	Partial Label Supervision for Agnostic Generative Noisy Label Learning
CAL	85.36	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels
Co-Teaching	83.83	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
CORES	83.60	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach
ELR	83.58	Early-Learning Regularization Prevents Memorization of Noisy Labels
JoCoR	83.37	Combating noisy labels by agreement: A joint training method with co-regularization
Co-Teaching+	83.26	How does Disagreement Help Generalization against Label Corruption?
Negative-LS	82.99	Understanding Generalized Label Smoothing when Learning with Noisy Labels
Positive-LS	82.76	Does label smoothing mitigate label noise?
F-div	82.53	When Optimizing $f$-divergence is Robust with Label Noise
Peer Loss	82.53	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
GCE	80.66	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels

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Learning With Noisy Labels On Cifar 10N Worst

Metrics

Results