Long-tail Learning on ImageNet-GLT

Metric: Accuracy (higher is better)

LeaderboardDataset

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#	Model↕	Accuracy▼	Augmentations	Paper	Date↕	Code
1	RIDE + IFL	45.64	No	Invariant Feature Learning for Generalized Long-...	2022-07-19	Code
2	RandAug + IFL	44.9	No	Invariant Feature Learning for Generalized Long-...	2022-07-19	Code
3	Logit-Adj + IFL	40.52	No	Invariant Feature Learning for Generalized Long-...	2022-07-19	Code
4	BLSoftmax + IFL	40.08	No	Invariant Feature Learning for Generalized Long-...	2022-07-19	Code
5	LDAM	38.54	No	Invariant Feature Learning for Generalized Long-...	2022-07-19	Code
6	cRT	37.57	No	Invariant Feature Learning for Generalized Long-...	2022-07-19	Code

#1RIDE + IFLSOTA
45.64
Accuracy· 2022-07-19
Invariant Feature Learning for Generalized Long-Tailed Classification Code
#2RandAug + IFL
44.9
Accuracy· 2022-07-19
Invariant Feature Learning for Generalized Long-Tailed Classification Code
#3Logit-Adj + IFL
40.52
Accuracy· 2022-07-19
Invariant Feature Learning for Generalized Long-Tailed Classification Code
#4BLSoftmax + IFL
40.08
Accuracy· 2022-07-19
Invariant Feature Learning for Generalized Long-Tailed Classification Code
#5LDAM
38.54
Accuracy· 2022-07-19
Invariant Feature Learning for Generalized Long-Tailed Classification Code
#6cRT
37.57
Accuracy· 2022-07-19
Invariant Feature Learning for Generalized Long-Tailed Classification Code