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 | RISDA | Error Rate | 20.11 | — | Unverified |
| 2 | Empirical Risk Minimization (ERM, CE) | Error Rate | 13.61 | — | Unverified |
| 3 | Class-balanced Reweighting | Error Rate | 13.46 | — | Unverified |
| 4 | Class-balanced Resampling | Error Rate | 13.21 | — | Unverified |
| 5 | IBLLoss | Error Rate | 12.93 | — | Unverified |
| 6 | Class-balanced Focal Loss | Error Rate | 12.9 | — | Unverified |
| 7 | DecTDE | Error Rate | 12.63 | — | Unverified |
| 8 | M2m | Error Rate | 12.5 | — | Unverified |
| 9 | Prior-LT | Error Rate | 12.2 | — | Unverified |
| 10 | ELF&LDAM+DRW | Error Rate | 12 | — | Unverified |