Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

Benchmark Model Rank Results
category-agnostic-pose-estimation-on-mp100MAML#2Mean PCK@0.2 - 1shot: 61.50
few-shot-image-classification-on-dirichletMAML#111:1 Accuracy: 47.6
few-shot-image-classification-on-dirichlet-1MAML#101:1 Accuracy: 64.5
few-shot-image-classification-on-meta-datasetfo-MAML#18Accuracy: 57.024
few-shot-image-classification-on-meta-dataset-1fo-MAML#10Mean Rank: 10.25
few-shot-image-classification-on-mini-12MAML + Transduction#12Accuracy: 31.8
few-shot-image-classification-on-mini-12MAML#13Accuracy: 31.3
few-shot-image-classification-on-mini-13MAML + Transduction#10Accuracy: 48.2
few-shot-image-classification-on-mini-13MAML#13Accuracy: 46.9
few-shot-image-classification-on-mini-2MAML#94Accuracy: 48.7
few-shot-image-classification-on-mini-3MAML#87Accuracy: 63.1
few-shot-image-classification-on-mini-5MAML (Finn et al., 2017)#8Accuracy: 40.15
few-shot-image-classification-on-omniglot-1-2MAML#10Accuracy: 98.7
few-shot-image-classification-on-omniglot-5-2MAML#2Accuracy: 99.9
few-shot-image-classification-on-tiered-2MAML + Transduction#11Accuracy: 34.8
few-shot-image-classification-on-tiered-2MAML#12Accuracy: 34.4
few-shot-image-classification-on-tiered-3MAML + Transduction#10Accuracy: 54.7
few-shot-image-classification-on-tiered-3MAML#11Accuracy: 53.3