Few-shot Image Classification on miniImageNet meta (test)
93.21AccuracyNAO-centroid
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| NAO-centroidFew-shot setting=5-way 5-shot2023.06 | 93.21 | — | — | |
| SeedSelectFew-shot setting=5-way 5-shot2023.06 | 92.08 | — | — | |
| DiffAlignFew-shot setting=5-way 5-shot, pre-trained on external datasets=true2023.06 | 88.63 | — | — | |
| FeLMiFew-shot setting=5-way 5-shot2023.06 | 86.08 | — | — | |
| Textual InversionFew-shot setting=5-way 5-shot, results from=[51], pre-trained on external datasets=true2023.06 | 85.44 | — | — | |
| Stable DiffusionFew-shot setting=5-way 5-shot, pre-trained on external datasets=true2023.06 | 85.05 | — | — | |
| SVAEFew-shot setting=5-way 5-shot, pre-trained on external datasets=true2023.06 | 80.7 | — | — | |
| MetaOptNet-SVM-trainvalbackbone=ResNet-12, training_set=meta-training + meta-validation2019.04 | 80 | — | — | |
| SEGAFew-shot setting=5-way 5-shot, multi-modal (class labels)=true2023.06 | 79.03 | — | — | |
| MetaOptNet-SVMbackbone=ResNet-122019.04 | 78.63 | — | — | |
| MetaOptNet-RRbackbone=ResNet-122019.04 | 77.88 | — | — | |
| LEObackbone=WRN-28-102019.04 | 77.59 | — | — | |
| TADAMbackbone=ResNet-122019.04 | 76.7 | — | — | |
| Activation to Parameterbackbone=WRN-28-102019.04 | 73.74 | — | — | |
| Dynamic Few-shotbackbone=64-64-128-1282019.04 | 73 | — | — | |
| AdaResNetbackbone=ResNet-122019.04 | 71.94 | — | — | |
| Transductive Prop Netsbackbone=64-64-64-642019.04 | 69.86 | — | — | |
| SNAILbackbone=ResNet-122019.04 | 68.88 | — | — | |
| R2D2backbone=96-192-384-5122019.04 | 68.8 | — | — | |
| Prototypical Networksbackbone=64-64-64-642019.04 | 68.2 | — | — | |
| Label-HallucFew-shot setting=5-way 5-shot2023.06 | 67.04 | — | — | |
| Relation Networksbackbone=64-96-128-2562019.04 | 65.32 | — | — | |
| MetaOptNet-SVM-trainvalbackbone=ResNet-12, training_set=meta-training + meta-validation2019.04 | 64.09 | — | — | |
| MAMLbackbone=32-32-32-322019.04 | 63.11 | — | — | |
| MetaOptNet-SVMbackbone=ResNet-122019.04 | 62.64 | — | — | |
| LEObackbone=WRN-28-102019.04 | 61.76 | — | — | |
| MetaOptNet-RRbackbone=ResNet-122019.04 | 61.41 | — | — | |
| Meta-Learning LSTMbackbone=64-64-64-642019.04 | 60.6 | — | — | |
| Activation to Parameterbackbone=WRN-28-102019.04 | 59.6 | — | — | |
| TADAMbackbone=ResNet-122019.04 | 58.5 | — | — | |
| AdaResNetbackbone=ResNet-122019.04 | 56.88 | — | — | |
| Dynamic Few-shotbackbone=64-64-128-1282019.04 | 56.2 | — | — | |
| SNAILbackbone=ResNet-122019.04 | 55.71 | — | — | |
| Transductive Prop Netsbackbone=64-64-64-642019.04 | 55.51 | — | — | |
| Matching Networksbackbone=64-64-64-642019.04 | 55.31 | — | — | |
| R2D2backbone=96-192-384-5122019.04 | 51.2 | — | — | |
| Relation Networksbackbone=64-96-128-2562019.04 | 50.44 | — | — | |
| ReptileN-way=5, K-shot=12019.09 | 49.97 | — | — | |
| Prototypical Networksbackbone=64-64-64-642019.04 | 49.42 | — | — | |
| iMAML HFN-way=5, K-shot=1, Inner loop optimizer=Hessian-free, CG steps=5, lambda=0.52019.09 | 49.3 | — | — | |
| iMAML GDN-way=5, K-shot=1, Inner loop gradient steps=10, CG steps=5, lambda=0.52019.09 | 48.96 | — | — | |
| MAMLN-way=5, K-shot=12019.09 | 48.7 | — | — | |
| MAMLbackbone=32-32-32-322019.04 | 48.7 | — | — | |
| first-order MAMLN-way=5, K-shot=12019.09 | 48.07 | — | — | |
| Matching Networksbackbone=64-64-64-642019.04 | 43.56 | — | — | |
| Meta-Learning LSTMbackbone=64-64-64-642019.04 | 43.44 | — | — | |
| Fine-tuningBackbone=ResNet-12, Number of meta-test tasks=6002021.11 | — | 60.56 | 76.42 | |
| l2-NormBackbone=ResNet-12, Number of meta-test tasks=6002021.11 | — | 60.93 | 76.57 | |
| l2-PGMBackbone=ResNet-12, Number of meta-test tasks=6002021.11 | — | 61.35 | 77.33 | |
| l2-SPBackbone=ResNet-12, Number of meta-test tasks=6002021.11 | — | 61.48 | 77.02 | |
| LSBackbone=ResNet-12, Number of meta-test tasks=6002021.11 | — | 61.31 | 76.73 | |
| REGSLBackbone=ResNet-12, Number of meta-test tasks=6002021.11 | — | 61.71 | 78.03 |