Few Shot Image Classification On Omniglot 5 2
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| learning-to-compare-relation-network-for-few | 99.8 |
| prototypical-networks-for-few-shot-learning | 99.7 |
| uncertainty-in-model-agnostic-meta-learning | 99.56% |
| how-to-train-your-maml | 99.85% |
| meta-curvature | 99.89 |
| rapid-adaptation-with-conditionally-shifted | 99.37 |
| meta-learning-with-implicit-gradients | 99.74% |
| towards-a-neural-statistician | 99.5 |
| decoder-choice-network-for-meta-learning | 99.92% |
| decoder-choice-network-for-meta-learning | 99.89% |
| hyperbolic-image-embeddings | 99.4 |
| matching-networks-for-one-shot-learning | 98.9 |
| learning-to-remember-rare-events | 99.6 |
| model-agnostic-meta-learning-for-fast | 99.9 |
| adaptive-posterior-learning-few-shot-learning | 99.9 |
| on-first-order-meta-learning-algorithms | 99.48 |