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 | Cross-Entropy (CE) | Error Rate | 62.75 | — | Unverified |
| 2 | Cross-Entropy (CE) | Error Rate | 61.68 | — | Unverified |
| 3 | IBLLoss | Error Rate | 61.52 | — | Unverified |
| 4 | Cross-Entropy + Curvature Regularization | Error Rate | 59.5 | — | Unverified |
| 5 | CE-DRW | Error Rate | 58.9 | — | Unverified |
| 6 | LDAM-DRW | Error Rate | 57.96 | — | Unverified |
| 7 | ELP | Error Rate | 57.6 | — | Unverified |
| 8 | CDB-loss | Error Rate | 57.43 | — | Unverified |
| 9 | CE-DRW-IC | Error Rate | 56.9 | — | Unverified |
| 10 | LDAM-DRW + SSP | Error Rate | 56.57 | — | Unverified |