Long-tail Learning
Long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing models from a large number of images that follow a long-tailed class distribution.
Papers
Showing 1–10 of 131 papers
All datasetsImageNet-LTCIFAR-100-LT (ρ=100)CIFAR-10-LT (ρ=10)iNaturalist 2018CIFAR-100-LT (ρ=10)Places-LTCIFAR-10-LT (ρ=100)CIFAR-100-LT (ρ=50)MIMIC-CXR-LTNIH-CXR-LTCOCO-MLTVOC-MLT
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ETF Classifier + DR (Resnet) | Error Rate | 23.5 | — | Unverified |
| 2 | LDAM-DRW | Error Rate | 22.97 | — | Unverified |
| 3 | LDAM-DRW + SSP | Error Rate | 22.17 | — | Unverified |
| 4 | ELP | Error Rate | 22 | — | Unverified |
| 5 | TSC(ResNet-32) | Error Rate | 21.3 | — | Unverified |
| 6 | CE+DRS+GIT | Error Rate | 21.24 | — | Unverified |
| 7 | smDRAGON | Error Rate | 20.37 | — | Unverified |
| 8 | TLC (4 experts) | Error Rate | 19.6 | — | Unverified |
| 9 | MetaSAug-LDAM | Error Rate | 19.34 | — | Unverified |
| 10 | ACE (4 experts) | Error Rate | 18.6 | — | Unverified |