HyperAI超神经

Image Classification On Clothing1M Using

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
CurriculumNet	81.5%	CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
Forward	80.27	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
CleanNet w_soft	79.90	CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
EMLC (k=1)	79.35%	Enhanced Meta Label Correction for Coping with Label Corruption
DMLP-DivideMix	78.23%	Learning from Noisy Labels with Decoupled Meta Label Purifier
FasTEN	77.83%	Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
PUDistill	77.70	Training Classifiers that are Universally Robust to All Label Noise Levels
L2B (ResNet-18)	77.5 ± 0.2%	L2B: Learning to Bootstrap Robust Models for Combating Label Noise
MLC	75.78%	Meta Label Correction for Noisy Label Learning

0 of 9 row(s) selected.

HyperAI超神经

Image Classification On Clothing1M Using

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

		Paper Title
CurriculumNet	81.5%	CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
Forward	80.27	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach
CleanNet w_soft	79.90	CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
EMLC (k=1)	79.35%	Enhanced Meta Label Correction for Coping with Label Corruption
DMLP-DivideMix	78.23%	Learning from Noisy Labels with Decoupled Meta Label Purifier
FasTEN	77.83%	Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection
PUDistill	77.70	Training Classifiers that are Universally Robust to All Label Noise Levels
L2B (ResNet-18)	77.5 ± 0.2%	L2B: Learning to Bootstrap Robust Models for Combating Label Noise
MLC	75.78%	Meta Label Correction for Noisy Label Learning

0 of 9 row(s) selected.

Image Classification On Clothing1M Using | SOTA | HyperAI超神经