Long Tail Learning On Cifar 10 Lt R 50

Metrics

Error Rate

Results

Performance results of various models on this benchmark

		Paper Title
MetaSAug-LDAM	15.66	MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
NCL(ResNet32)	13.2	Nested Collaborative Learning for Long-Tailed Visual Recognition
ConCutMix	12.0	Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation
MDCS	11.7	MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
OPeN (WideResNet-28-10)	10.8	Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
DeiT-LT	10.2	DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
SURE(ResNet-32)	9.78	SURE: SUrvey REcipes for building reliable and robust deep networks
GLMC + SAM	8.44	Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data

0 of 8 row(s) selected.

Long Tail Learning On Cifar 10 Lt R 50

Metrics

Error Rate

Results

Performance results of various models on this benchmark

		Paper Title
MetaSAug-LDAM	15.66	MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition
NCL(ResNet32)	13.2	Nested Collaborative Learning for Long-Tailed Visual Recognition
ConCutMix	12.0	Enhanced Long-Tailed Recognition with Contrastive CutMix Augmentation
MDCS	11.7	MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
OPeN (WideResNet-28-10)	10.8	Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
DeiT-LT	10.2	DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed Datasets
SURE(ResNet-32)	9.78	SURE: SUrvey REcipes for building reliable and robust deep networks
GLMC + SAM	8.44	Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data

0 of 8 row(s) selected.

Long Tail Learning On Cifar 10 Lt R 50 | SOTA | HyperAI