| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Image Classification | CIFAR10 (test) | Accuracy99.5 | 585 | |
| Classification | CIFAR10 (test) | Accuracy97.42 | 331 | |
| Image Generation | CIFAR10 32x32 (test) | FID1.1 | 186 | |
| Image Classification | CIFAR10 non-iid | Accuracy88.1 | 162 | |
| Graph Classification | CIFAR10 (test) | Test Accuracy76.903 | 162 | |
| Image Classification | CIFAR10 LT (test) | Accuracy86.7 | 106 | |
| Two-Sample Testing | CIFAR10-RES18 (test) | Test Power100 | 97 | |
| Image Classification Calibration | CIFAR10 | Classwise ECE0.33 | 84 | |
| Image Generation | CIFAR10 50k samples (test) | FID1.79 | 81 | |
| Image Generation | CIFAR10 (train) | FID0.02 | 77 | |
| Image Classification | CIFAR10 (test) | Accuracy98.1 | 76 | |
| Image Classification | CIFAR10-C (test) | Accuracy (Gaussian)89.94 | 72 | |
| Image Classification | CIFAR10 long-tailed (test) | Accuracy85.5 | 68 | |
| Image Classification | CIFAR10 IDN (test) | Accuracy96.68 | 67 | |
| Continual Learning | Split CIFAR10 32x32 (test) | Accuracy60.1 | 66 | |
| Sequential Image Classification | Sequential CIFAR10 | Accuracy91.13 | 60 | |
| Image Classification | CIFAR10-C | Mean Accuracy (mAcc)84.68 | 52 | |
| Image Classification | CIFAR10 (val) | Accuracy99.29 | 51 | |
| Image Classification | CIFAR10 5 tasks (test) | Accuracy69.1 | 51 | |
| Continual Learning | CIFAR10 5 tasks (test) | Avg Forgetting Rate10 | 51 | |
| Two-Sample Testing | CIFAR10-WRN8 | Test Power100 | 49 | |
| Two-Sample Testing | CIFAR10 WRN28 | Test Power66.9 | 49 | |
| Image Classification | CIFAR10 (test) | Test Accuracy94.7 | 49 | |
| Image Classification | CIFAR10 | Training Loss0.02 | 48 | |
| Data Valuation | CIFAR10 | Provider Influence (Mean)0.508 | 48 |