Image Classification on Clothing1M (using clean data)

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Accuracy

Rank	Model	Accuracy	Extra Training Data	Paper	Code	Year
1	CurriculumNet	81.5%	✓	CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images	MalongTech/CurriculumNet · guoshengcv/CurriculumNet	2018
2	Forward	80.27	✓	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach	yikun2019/PENCIL · giorgiop/loss-correction	2016
3	CleanNet w_soft	79.90	✓	CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise	kuanghuei/clean-net · YutingLi0606/SURE · yingyichen-cyy/JigsawViT	2017
4	EMLC (k=1)	79.35%		Enhanced Meta Label Correction for Coping with Label Corruption	MitchellKT/Enhanced-Meta-Label-Correction	2023
5	DMLP-DivideMix	78.23%		Learning from Noisy Labels with Decoupled Meta Label Purifier	yuanpengtu/DMLP	2023
6	FasTEN	77.83%		Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection	hyperconnect/fasten	2021
7	PUDistill	77.70	✓	Training Classifiers that are Universally Robust to All Label Noise Levels	Xu-Jingyi/PUDistill	2021
8	L2B (ResNet-18)	77.5 ± 0.2%	✓	L2B: Learning to Bootstrap Robust Models for Combating Label Noise	yuyinzhou/l2b	2022
9	MLC	75.78%	✓	Meta Label Correction for Noisy Label Learning	microsoft/mlc	2019