Learning With Noisy Labels On Cifar 10N 1

평가 지표

Accuracy (mean)

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	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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