| few-shot-image-classification-on-mini-imagenet-cub-5-way-1-shot | MatchingNet (Vinyals et al., 2016) | #4 | Accuracy: 45.59 |
| few-shot-image-classification-on-stanford-cars-5-way-1-shot | Matching Nets FCE++ | #4 | Accuracy: 34.80 |
| few-shot-image-classification-on-stanford-cars-5-way-5-shot | Matching Nets FCE++ | #4 | Accuracy: 44.70 |
| few-shot-image-classification-on-stanford-dogs-5-way-5-shot | Matching Nets FCE++ | #4 | Accuracy: 47.50 |
| few-shot-image-classification-on-meta-dataset-rank | Matching Networks | #11 | Mean Rank: 10.5 |
| few-shot-image-classification-on-omniglot-5-shot-20-way | Matching Nets | #13 | Accuracy: 98.5% |
| few-shot-image-classification-on-omniglot-1-shot-20-way | Matching Nets | #14 | Accuracy: 93.8% |
| few-shot-image-classification-on-omniglot-1-shot-5-way | Matching Nets | #14 | Accuracy: 98.1 |
| few-shot-image-classification-on-omniglot-5-shot-5-way | Matching Nets | #15 | Accuracy: 98.9 |
| few-shot-image-classification-on-meta-dataset | Matching Networks | #19 | Accuracy: 56.247 |
| few-shot-image-classification-on-mini-imagenet-5-way-5-shot | Matching Nets (Cosine Matching Fn) | #89 | Accuracy: 60 |
| few-shot-image-classification-on-mini-imagenet-5-way-1-shot | Matching Nets (Cosine Matching Fn) | #96 | Accuracy: 46.6 |