Few Shot Image Classification On Omniglot 1 2
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy |
|---|---|
| meta-learning-with-implicit-gradients | 99.50 |
| matching-networks-for-one-shot-learning | 98.1 |
| learning-to-compare-relation-network-for-few | 99.6 |
| how-to-train-your-maml | 99.47 |
| model-agnostic-meta-learning-for-fast | 98.7 |
| rapid-adaptation-with-conditionally-shifted | 98.42 |
| uncertainty-in-model-agnostic-meta-learning | 98.43 |
| decoder-choice-network-for-meta-learning | 99.8% |
| prototypical-networks-for-few-shot-learning | 98.8 |
| gradient-based-meta-learning-with-learned | 99.5 |
| learning-to-remember-rare-events | 98.4 |
| adaptive-posterior-learning-few-shot-learning | 97.9 |
| meta-curvature | 99.97 |
| hyperbolic-image-embeddings | 99.0 |
| towards-a-neural-statistician | 98.1 |
| on-first-order-meta-learning-algorithms | 97.68 |
| decoder-choice-network-for-meta-learning | 99.92% |