Long-tail Learning on ImageNet-LT-d

Metric: Per-Class Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Per-Class Accuracy▼	Augmentations	Paper	Date↕	Code
1	DRAGON + Bal'Loss	53.5	No	From Generalized zero-shot learning to long-tail...	2020-04-05	Code
2	DRAGON	51.2	No	From Generalized zero-shot learning to long-tail...	2020-04-05	Code
3	LWS	49.9	No	Decoupling Representation and Classifier for Lon...	2019-10-21	Code
4	CADA-VAE	49.3	No	-	-	Code
5	OLTR	37.7	No	Large-Scale Long-Tailed Recognition in an Open W...	2019-04-10	Code

#1DRAGON + Bal'LossSOTA
53.5
Per-Class Accuracy· 2020-04-05
From Generalized zero-shot learning to long-tail with class descriptors Code
#2DRAGON
51.2
Per-Class Accuracy· 2020-04-05
From Generalized zero-shot learning to long-tail with class descriptors Code
#3LWSSOTA
49.9
Per-Class Accuracy· 2019-10-21
Decoupling Representation and Classifier for Long-Tailed Recognition Code
#4CADA-VAE
49.3
Per-Class Accuracy
No paperCode
#5OLTRSOTA
37.7
Per-Class Accuracy· 2019-04-10
Large-Scale Long-Tailed Recognition in an Open World Code