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 | Decoupling (cRT) | Balanced Accuracy | 0.29 | — | Unverified |
| 2 | Reweighted LDAM-DRW | Balanced Accuracy | 0.29 | — | Unverified |
| 3 | Class-balanced LDAM-DRW | Balanced Accuracy | 0.28 | — | Unverified |
| 4 | Reweighted LDAM | Balanced Accuracy | 0.28 | — | Unverified |
| 5 | Class-Balanced Softmax | Balanced Accuracy | 0.27 | — | Unverified |
| 6 | Reweighted Softmax | Balanced Accuracy | 0.26 | — | Unverified |
| 7 | Class-balanced LDAM | Balanced Accuracy | 0.24 | — | Unverified |
| 8 | Class-Balanced Focal Loss | Balanced Accuracy | 0.23 | — | Unverified |
| 9 | Decoupling (tau-norm) | Balanced Accuracy | 0.21 | — | Unverified |
| 10 | Reweighted Focal Loss | Balanced Accuracy | 0.2 | — | Unverified |