5-way Few-shot Classification on CUB (test)
82.9AccuracyFEAT
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| FEATBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 82.9 | — | — | |
| Hyperbolic ProtoNetEmbedding Net=4 Conv2019.04 | 82.53 | — | — | |
| DN4-DAEmbedding Net=4 Conv2019.04 | 81.9 | — | — | |
| ProtoNetBackbone=ConvNet, Setup=Instance Embedding, Test trials=100002018.12 | 81.5 | — | — | |
| GCNBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 80.26 | — | — | |
| DEEPSETSBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 79.65 | — | — | |
| Baseline++Embedding Net=4 Conv2019.04 | 79.34 | — | — | |
| MatchNetBackbone=ConvNet, Setup=Instance Embedding, Test trials=100002018.12 | 79 | — | — | |
| RelationNetEmbedding Net=4 Conv2019.04 | 76.11 | — | — | |
| RelationNetBackbone=ConvNet2018.12 | 76.11 | — | — | |
| MACOEmbedding Net=4 Conv2019.04 | 74.96 | — | — | |
| BILSTMBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 73.51 | — | — | |
| MatchingNetEmbedding Net=4 Conv2019.04 | 72.86 | — | — | |
| MatchNetBackbone=ConvNet2018.12 | 72.86 | — | — | |
| MAMLEmbedding Net=4 Conv2019.04 | 72.09 | — | — | |
| MAMLBackbone=ConvNet2018.12 | 72.09 | — | — | |
| ProtoNetEmbedding Net=4 Conv2019.04 | 70.77 | — | — | |
| ProtoNetBackbone=ConvNet2018.12 | 70.77 | — | — | |
| FEATBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 68.87 | — | — | |
| GCNBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 67.83 | — | — | |
| MatchNetBackbone=ConvNet, Setup=Instance Embedding, Test trials=100002018.12 | 67.73 | — | — | |
| DEEPSETSBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 67.22 | — | — | |
| Hyperbolic ProtoNetEmbedding Net=4 Conv2019.04 | 64.02 | — | — | |
| ProtoNetBackbone=ConvNet, Setup=Instance Embedding, Test trials=100002018.12 | 63.72 | — | — | |
| RelationNetEmbedding Net=4 Conv2019.04 | 62.45 | — | — | |
| RelationNetBackbone=ConvNet2018.12 | 62.45 | — | — | |
| BILSTMBackbone=ConvNet, Setup=Embedding Adaptation, Test trials=100002018.12 | 62.05 | — | — | |
| MatchingNetEmbedding Net=4 Conv2019.04 | 61.16 | — | — | |
| MatchNetBackbone=ConvNet2018.12 | 61.16 | — | — | |
| MACOEmbedding Net=4 Conv2019.04 | 60.76 | — | — | |
| Baseline++Embedding Net=4 Conv2019.04 | 60.53 | — | — | |
| MAMLEmbedding Net=4 Conv2019.04 | 55.92 | — | — | |
| MAMLBackbone=ConvNet2018.12 | 55.92 | — | — | |
| DN4-DAEmbedding Net=4 Conv2019.04 | 53.15 | — | — | |
| ProtoNetEmbedding Net=4 Conv2019.04 | 51.31 | — | — | |
| ProtoNetBackbone=ConvNet2018.12 | 51.31 | — | — | |
| Baseline2019.05 | — | 47.12 | 64.16 | |
| BaselineSetting=Inductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 65.51 | 82.85 | |
| Baseline ++2019.05 | — | 60.53 | 79.34 | |
| Baseline++Setting=Inductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 67.02 | 83.58 | |
| LRSetting=Inductive, Backbone=ResNet-12, Input Size=84x842020.03 | — | 76.16 | 90.32 | |
| LR + ICISetting=Transductive, Backbone=ResNet-12, Input Size=84x842020.03 | — | 88.06 | 92.53 | |
| LR + ICISetting=Semi-supervised, Backbone=ResNet-12, Input Size=84x84, Unlabeled Samples=15/152020.03 | — | 87.28 | 92.18 | |
| LR + ICISetting=Semi-supervised, Backbone=ResNet-12, Input Size=84x84, Unlabeled Samples=30/502020.03 | — | 89.58 | 92.48 | |
| LR + ICISetting=Semi-supervised, Backbone=ResNet-12, Input Size=84x84, Unlabeled Samples=80/802020.03 | — | 91.11 | 92.98 | |
| MAML2019.05 | — | 55.92 | 72.09 | |
| MAMLSetting=Inductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 69.96 | 82.7 | |
| MAML++Complexity=Low-End2019.05 | — | 62.19 | 76.08 | |
| MAML++Complexity=High-End2019.05 | — | 67.48 | 83.8 | |
| MAML++ + SCAComplexity=Low-End2019.05 | — | 66.13 | 77.62 | |
| MAML++ + SCAComplexity=High-End2019.05 | — | 70.46 | 85.63 | |
| Matching networks2019.05 | — | 61.16 | 72.86 | |
| MatchingNetSetting=Inductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 72.36 | 83.64 | |
| Nearest neighbor (baseline)Features=ImageNet model features (trained on disjoint categories)2018.06 | — | 58.7 | 80.2 | |
| ProtoNetSetting=Inductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 71.88 | 87.42 | |
| Prototypical NetworksFeatures=ImageNet model features (trained on disjoint categories)2018.06 | — | 71.9 | 92.4 | |
| RelationNetSetting=Inductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 67.59 | 82.75 | |
| SVMSetting=Inductive, Backbone=ResNet-12, Input Size=84x842020.03 | — | 75.84 | 89.26 | |
| SVM + ICISetting=Transductive, Backbone=ResNet-12, Input Size=84x842020.03 | — | 87.87 | 92.38 | |
| SVM + ICISetting=Semi-supervised, Backbone=ResNet-12, Input Size=84x84, Unlabeled Samples=15/152020.03 | — | 86.83 | 91.58 | |
| SVM + ICISetting=Semi-supervised, Backbone=ResNet-12, Input Size=84x84, Unlabeled Samples=30/502020.03 | — | 88.94 | 92.14 | |
| TEAMSetting=Transductive, Backbone=ResNet-18, Input Size=224x2242020.03 | — | 80.16 | 87.17 | |
| Δ-encoderFeatures=ImageNet model features (trained on disjoint categories)2018.06 | — | 82.2 | 92.6 |