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 | CE-DRW-IC | Error Rate | 41.4 | — | Unverified |
| 2 | LDAM-DRW | Error Rate | 41.29 | — | Unverified |
| 3 | CDB-loss | Error Rate | 41.26 | — | Unverified |
| 4 | smDRAGON | Error Rate | 41.23 | — | Unverified |
| 5 | LDAM-DRW + SSP | Error Rate | 41.09 | — | Unverified |
| 6 | ELP | Error Rate | 40.9 | — | Unverified |
| 7 | CBD+TailCalibX | Error Rate | 38.87 | — | Unverified |
| 8 | UniMix+Bayias (ResNet-32) | Error Rate | 38.75 | — | Unverified |
| 9 | MetaSAug-LDAM | Error Rate | 38.72 | — | Unverified |
| 10 | RIDE + CMO + Curvature Regularization | Error Rate | 38.6 | — | Unverified |