Matching Networks for One Shot Learning

Benchmark Model Rank Results
few-shot-image-classification-on-meta-datasetMatching Networks#19Accuracy: 56.247
few-shot-image-classification-on-meta-dataset-1Matching Networks#11Mean Rank: 10.5
few-shot-image-classification-on-mini-2Matching Nets (Cosine Matching Fn)#95Accuracy: 46.6
few-shot-image-classification-on-mini-3Matching Nets (Cosine Matching Fn)#88Accuracy: 60
few-shot-image-classification-on-mini-5MatchingNet (Vinyals et al., 2016)#4Accuracy: 45.59
few-shot-image-classification-on-omniglot-1-1Matching Nets#14Accuracy: 93.8%
few-shot-image-classification-on-omniglot-1-2Matching Nets#14Accuracy: 98.1
few-shot-image-classification-on-omniglot-5-1Matching Nets#13Accuracy: 98.5%
few-shot-image-classification-on-omniglot-5-2Matching Nets#15Accuracy: 98.9
few-shot-image-classification-on-stanford-1Matching Nets FCE++#4Accuracy: 47.50
few-shot-image-classification-on-stanford-2Matching Nets FCE++#4Accuracy: 34.80
few-shot-image-classification-on-stanford-3Matching Nets FCE++#4Accuracy: 44.70