MTIL
Benchmarks
| Task Name | Dataset Name | SOTA Result | Trend | |
|---|---|---|---|---|
| Multi-Task Incremental Learning | MTIL Order II | Average Acc89.2 | 76 | |
| Multi-domain Task-Incremental Learning | MTIL Order I 5-shot (test) | Accuracy (Caltech101)95.8 | 46 | |
| Multi-Task Incremental Learning | MTIL Aircraft, Caltech101, CIFAR100, DTD, EuroSAT, Flowers, Food, MNIST, OxfordPet, Cars, SUN397 | Caltech101 Accuracy96.8 | 32 | |
| Multi-domain Task-Incremental Learning | MTIL Order I (test) | Average Accuracy79.1 | 30 | |
| Multi-Task Incremental Learning | MTIL few-shot Order II (test) | Accuracy (Food)88.8 | 20 | |
| Multi-Task Incremental Learning | MTIL | Average Accuracy85.9 | 20 | |
| Image Classification | MTIL task-agnostic (test) | Aircraft Accuracy62 | 20 | |
| Continual Learning | MTIL | FoM242.4 | 18 | |
| Multi-domain Task-Incremental Learning | MTIL Order I | Transfer Acc70.4 | 17 | |
| Multi-Task Incremental Learning | MTIL Aircraft, Caltech101, CIFAR100, DTD, EuroSAT, Flowers, Food, MNIST, OxfordPet, Cars, SUN397 | Aircraft Score59.6 | 15 | |
| Few-Shot Multi-Task Incremental Learning | MTIL FS OrderI | Transfer Value69.9 | 9 | |
| Multi-domain Task Incremental Learning | MTIL Last 1.0 (test) | Accuracy (Aircraft)62 | 8 | |
| Multi-domain Task Incremental Learning | MTIL Average 1.0 (test) | Accuracy (Aircraft)62 | 8 | |
| Multi-domain Task Incremental Learning | MTIL benchmark Transfer 1.0 (test) | Caltech101 Accuracy95.1 | 8 | |
| Multi-Task Incremental Learning | MTIL benchmark | Inference Time3 | 4 | |
| Multi-Task Incremental Learning | MTIL-FS 16-shot (test) | Transfer Accuracy69.6 | 3 |
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