5-way Image Classification on CIFAR FS (test)
86.1AccuracyGidaris et al.
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| Gidaris et al.Backbone=WRN-28-10, Shot=5-shot2020.04 | 86.1 | — | — | |
| SIBBackbone=WRN-28-10, Shot=5-shot, K=3, learning_rate=1e-32020.04 | 85.3 | — | — | |
| SIBBackbone=WRN-28-10, Shot=5-shot, K=5, learning_rate=1e-32020.04 | 85.3 | — | — | |
| MetaOptNet-SVM-trainvalbackbone=ResNet-12, shot=5-shot, training_set=meta-train + meta-validation2019.04 | 85 | — | — | |
| SIBBackbone=WRN-28-10, Shot=5-shot, K=1, learning_rate=1e-32020.04 | 84.9 | — | — | |
| MetaOptNet-RRbackbone=ResNet-12, shot=5-shot2019.04 | 84.3 | — | — | |
| MetaOptNet-RRBackbone=ResNet-12, Shot=5-shot2020.04 | 84.3 | — | — | |
| MetaOptNet-SVMbackbone=ResNet-12, shot=5-shot2019.04 | 84.2 | — | — | |
| MetaOptNet-SVMBackbone=ResNet-12, Shot=5-shot2020.04 | 84.2 | — | — | |
| ProtoNets (our backbone)backbone=ResNet-12, shot=5-shot2019.04 | 83.5 | — | — | |
| SIBBackbone=WRN-28-10, Shot=5-shot, K=0, Feature=Pre-trained2020.04 | 83.5 | — | — | |
| MTUNetNumber of shots=5-shot, Backbone=WRN-28-102020.11 | 82.93 | — | — | |
| MTUNetNumber of shots=5-shot, Backbone=ResNet-182020.11 | 80.16 | — | — | |
| SIBBackbone=WRN-28-10, Shot=1-shot, K=5, learning_rate=1e-32020.04 | 80 | — | — | |
| Gidaris et al.Backbone=Conv-4-64, Shot=5-shot2020.04 | 79.8 | — | — | |
| R2D2backbone=96-192-384-512, shot=5-shot2019.04 | 79.4 | — | — | |
| SIBBackbone=WRN-28-10, Shot=1-shot, K=3, learning_rate=1e-32020.04 | 78.4 | — | — | |
| R2-D2Number of shots=5-shot2020.11 | 78.3 | — | — | |
| R2-D2Backbone=Conv-4-64, Shot=5-shot2020.04 | 77.4 | — | — | |
| SIBBackbone=Conv-4-64, Shot=5-shot, K=3, learning_rate=1e-32020.04 | 77.1 | — | — | |
| SIBBackbone=WRN-28-10, Shot=1-shot, K=1, learning_rate=1e-32020.04 | 76.8 | — | — | |
| SIBBackbone=Conv-4-64, Shot=5-shot, K=0, Feature=Pre-trained2020.04 | 75.4 | — | — | |
| GNNBackbone=Conv-4-64, Shot=5-shot2020.04 | 75.3 | — | — | |
| GNNNumber of shots=5-shot2020.11 | 75.3 | — | — | |
| Gidaris et al.Backbone=WRN-28-10, Shot=1-shot2020.04 | 73.6 | — | — | |
| MetaOptNet-SVM-trainvalbackbone=ResNet-12, shot=1-shot, training_set=meta-train + meta-validation2019.04 | 72.8 | — | — | |
| MetaOptNet-RRbackbone=ResNet-12, shot=1-shot2019.04 | 72.6 | — | — | |
| MetaOptNet-RRBackbone=ResNet-12, Shot=1-shot2020.04 | 72.6 | — | — | |
| ProtoNets (our backbone)backbone=ResNet-12, shot=1-shot2019.04 | 72.2 | — | — | |
| Prototypical Networksbackbone=64-64-64-64, shot=5-shot2019.04 | 72 | — | — | |
| MetaOptNet-SVMbackbone=ResNet-12, shot=1-shot2019.04 | 72 | — | — | |
| Prototypical NetBackbone=Conv-4-64, Shot=5-shot2020.04 | 72 | — | — | |
| MetaOptNet-SVMBackbone=ResNet-12, Shot=1-shot2020.04 | 72 | — | — | |
| ProtoNetNumber of shots=5-shot2020.11 | 72 | — | — | |
| MAMLbackbone=32-32-32-32, shot=5-shot2019.04 | 71.5 | — | — | |
| MAMLBackbone=Conv-4-64, Shot=5-shot2020.04 | 71.5 | — | — | |
| MAMLNumber of shots=5-shot2020.11 | 71.5 | — | — | |
| SIBBackbone=WRN-28-10, Shot=1-shot, K=0, Feature=Pre-trained2020.04 | 70 | — | — | |
| Relation Networksbackbone=64-96-128-256, shot=5-shot2019.04 | 69.3 | — | — | |
| Relation NetBackbone=Conv-4-64, Shot=5-shot2020.04 | 69.3 | — | — | |
| RelationNetNumber of shots=5-shot2020.11 | 69.3 | — | — | |
| SIBBackbone=Conv-4-64, Shot=1-shot, K=3, learning_rate=1e-32020.04 | 68.7 | — | — | |
| MTUNetNumber of shots=1-shot, Backbone=WRN-28-102020.11 | 68.34 | — | — | |
| MTUNetNumber of shots=1-shot, Backbone=ResNet-182020.11 | 66.31 | — | — | |
| R2D2backbone=96-192-384-512, shot=1-shot2019.04 | 65.3 | — | — | |
| R2-D2Number of shots=1-shot2020.11 | 65.3 | — | — | |
| Gidaris et al.Backbone=Conv-4-64, Shot=1-shot2020.04 | 63.5 | — | — | |
| R2-D2Backbone=Conv-4-64, Shot=1-shot2020.04 | 62.3 | — | — | |
| GNNBackbone=Conv-4-64, Shot=1-shot2020.04 | 61.9 | — | — | |
| GNNNumber of shots=1-shot2020.11 | 61.9 | — | — | |
| SIBBackbone=Conv-4-64, Shot=1-shot, K=0, Feature=Pre-trained2020.04 | 59.2 | — | — | |
| MAMLbackbone=32-32-32-32, shot=1-shot2019.04 | 58.9 | — | — | |
| MAMLBackbone=Conv-4-64, Shot=1-shot2020.04 | 58.9 | — | — | |
| MAMLNumber of shots=1-shot2020.11 | 58.9 | — | — | |
| Prototypical Networksbackbone=64-64-64-64, shot=1-shot2019.04 | 55.5 | — | — | |
| Prototypical NetBackbone=Conv-4-64, Shot=1-shot2020.04 | 55.5 | — | — | |
| ProtoNetNumber of shots=1-shot2020.11 | 55.5 | — | — | |
| Relation Networksbackbone=64-96-128-256, shot=1-shot2019.04 | 55 | — | — | |
| Relation NetBackbone=Conv-4-64, Shot=1-shot2020.04 | 55 | — | — | |
| RelationNetNumber of shots=1-shot2020.11 | 55 | — | — | |
| Baseline++Backbone=WRN-28-10, N-way=5-way2019.07 | — | 67.5 | 80.08 | |
| DCOBackbone=WRN-28-10, N-way=5-way2019.07 | — | 72 | 84.2 | |
| DPGNbackbone=ConvNet, transduction=true2020.03 | — | 76.4 | 88.4 | |
| DPGNbackbone=ResNet12, transduction=true2020.03 | — | 77.9 | 90.2 | |
| Fine-tuningArchitecture=WRN-28-10, Pre-training Split=train2019.09 | — | 68.72 | 86.11 | |
| Fine-tuningArchitecture=WRN-28-10, Pre-training Split=train + val2019.09 | — | 70.07 | 87.26 | |
| GNNBackbone=Conv4-64, Type=Metric2020.09 | — | 61.9 | 75.3 | |
| MAMLBackbone=WRN-28-10, N-way=5-way2019.07 | — | 58.9 | 71.5 | |
| MAMLbackbone=ConvNet2020.03 | — | 58.9 | 71.5 | |
| MAMLBackbone=Conv4-32, Type=Meta2020.09 | — | 58.9 | 71.5 | |
| Manifold MixupBackbone=WRN-28-10, N-way=5-way2019.07 | — | 69.2 | 83.42 | |
| MetaOpt SVMArchitecture=ResNet-12, Pre-training Split=train2019.09 | — | 72 | 84.2 | |
| MetaOpt SVMArchitecture=ResNet-12*, Pre-training Split=train + val2019.09 | — | 72.8 | 85 | |
| MetaOptNetbackbone=ResNet122020.03 | — | 72 | 84.2 | |
| ProtoNetBackbone=WRN-28-10, N-way=5-way2019.07 | — | 55.5 | 72 | |
| ProtoNetbackbone=ConvNet2020.03 | — | 55.5 | 72 | |
| PROTOTYPICAL NETSBackbone=Conv4-64, Type=Metric2020.09 | — | 55.5 | 72 | |
| R2-D2Backbone=Conv4-512, Type=Metric2020.09 | — | 65.3 | 79.4 | |
| R2D2backbone=ConvNet2020.03 | — | 65.3 | 79.4 | |
| R2D2Architecture=conv (96)x42019.09 | — | 65.4 | 79.4 | |
| RCNBackbone=Conv4-64, Type=Metric2020.09 | — | 61.61 | 77.63 | |
| RCNBackbone=Res12, Type=Metric2020.09 | — | 69.02 | 82.96 | |
| RELATION NETSBackbone=Conv4-64, Type=Metric2020.09 | — | 55 | 69.3 | |
| RelationNetBackbone=WRN-28-10, N-way=5-way2019.07 | — | 55 | 69.3 | |
| RelationNetbackbone=ConvNet2020.03 | — | 55 | 69.3 | |
| RotationBackbone=WRN-28-10, N-way=5-way2019.07 | — | 70.66 | 84.15 | |
| S2M2_RBackbone=WRN-28-10, N-way=5-way2019.07 | — | 74.81 | 87.47 | |
| S2M2_RBackbone=ResNet-18, N-way=5-way2019.07 | — | 63.66 | 76.07 | |
| S2M2_RBackbone=ResNet-34, N-way=5-way2019.07 | — | 62.77 | 75.75 | |
| Shot-Freebackbone=ResNet122020.03 | — | 69.2 | 84.7 | |
| Support-based initializationArchitecture=WRN-28-10, Pre-training Split=train2019.09 | — | 70.26 | 83.82 | |
| Support-based initializationArchitecture=WRN-28-10, Pre-training Split=train + val2019.09 | — | 72.14 | 85.21 | |
| Transductive fine-tuningArchitecture=WRN-28-10, Pre-training Split=train2019.09 | — | 76.58 | 85.79 | |
| Transductive fine-tuningArchitecture=WRN-28-10, Pre-training Split=train + val2019.09 | — | 78.36 | 87.54 |