Few-shot classification on CUB (test) (Meta-trained on Mini-ImageNet)
80.92Accuracy (5-shot)FCAM
Evaluation Results
| Method | Links | ||||
|---|---|---|---|---|---|
| FCAM2024.07 | 80.92 | — | 58.2 | — | |
| TRIDENTBackbone=Conv4, Approach=transductive feature extraction2022.08 | 80.74 | — | 84.61 | — | |
| PEME-BMSBackbone=WRN, Approach=transductive feature extraction and inference2022.08 | 79.15 | — | 63.9 | — | |
| DeepEMD v22024.07 | 78.86 | — | 54.24 | — | |
| MCL-KatzBackbone=ResNet-122021.06 | 77.39 | — | 53.22 | — | |
| FRNBackbone=RN12, Approach=inductive inference2022.08 | 77.09 | — | 54.11 | — | |
| PT-MAPSetting=Transductive, Backbone=WRN2020.06 | 76.51 | — | 62.49 | — | |
| PT+MAPBackbone=WRN, Approach=transductive feature extraction and inference2022.08 | 76.51 | — | 62.49 | — | |
| MCLBackbone=ResNet-122021.06 | 75.93 | — | 55.48 | — | |
| Transfer+SGCSetting=Transductive, Backbone=WRN2020.06 | 73.46 | — | 58.63 | — | |
| linear classifier2024.07 | 73.3 | — | 50.37 | — | |
| FRNBackbone=ResNet-122021.06 | 72.97 | — | 51.6 | — | |
| Assoc-AlignBackbone=WRN, Approach=transductive feature extraction2022.08 | 72.37 | — | 47.25 | — | |
| ProtoNet2024.07 | 72.02 | — | 50.01 | — | |
| MetaQDABackbone=WRN, Approach=transductive feature extraction2022.08 | 71.84 | — | 53.75 | — | |
| MetaQDABackbone=WRN2021.01 | 71.84 | — | 53.75 | — | |
| KNN2024.07 | 71.25 | — | 50.84 | — | |
| FEATBackbone=RN18, Approach=transductive feature extraction2022.08 | 71.08 | — | 50.67 | — | |
| TIM-GDBackbone=WRN, Approach=transductive inference2022.08 | 71 | — | — | — | |
| S2M2_RSetting=Inductive, Backbone=WRN2020.06 | 70.44 | — | 48.24 | — | |
| S2M2Backbone=WRN, Approach=transductive feature extraction2022.08 | 70.44 | — | 48.24 | — | |
| S2M2Backbone=WRN2021.01 | 70.44 | — | 48.24 | — | |
| Centroid et al.Backbone=ResNet-182021.06 | 70.37 | — | 46.85 | — | |
| PT-NCMSetting=Inductive, Backbone=WRN2020.06 | 70.22 | — | 48.37 | — | |
| Neg-marginBackbone=ResNet-182021.06 | 69.3 | — | — | — | |
| MatchNet2024.07 | 69.14 | — | 51.65 | — | |
| cosine classifier2024.07 | 69.01 | — | 44.17 | — | |
| MetaQDABackbone=ResNet-182021.01 | 68.59 | — | 48.88 | — | |
| GNN+FTBackbone=ResNet-102021.06 | 66.98 | — | 47.47 | — | |
| LFWTBackbone=RN10, Approach=transductive feature extraction and inference2022.08 | 66.98 | — | 47.47 | — | |
| LFWTBackbone=ResNet-102021.01 | 66.98 | — | 47.47 | — | |
| SIMPLESHOT†Backbone=WRN2021.01 | 66.77 | — | 49.65 | — | |
| LRP (CAN)Backbone=ResNet-122021.01 | 66.58 | — | 46.23 | — | |
| LaplacianShotBackbone=WRN, Approach=transductive inference2022.08 | 66.33 | — | 55.46 | — | |
| Manifold MixupSetting=Inductive, Backbone=WRN2020.06 | 66.03 | — | 46.21 | — | |
| SimpleShotBackbone=WRN, Approach=inductive inference2022.08 | 65.63 | — | 48.56 | — | |
| BaselineBackbone=ResNet-102021.06 | 65.57 | — | — | — | |
| SIMPLESHOT†Backbone=ResNet-182021.01 | 65.56 | — | 46.68 | — | |
| MetaOptNetBackbone=ResNet-122021.06 | 64.98 | — | 44.79 | — | |
| ProtoNet+MCLBackbone=ResNet-122021.06 | 64.76 | — | 42.02 | — | |
| LRP (GNN)Backbone=ResNet-102021.01 | 64.44 | — | 48.29 | — | |
| MetaQDABackbone=Conv-42021.01 | 64.4 | — | 47.25 | — | |
| BaselineBackbone=WRN-28-10, Training Paradigm=Supervised, Evaluation Protocol=5-way2022.10 | 62.12 | — | 42.89 | — | |
| Baseline++Backbone=ResNet-182021.06 | 62.04 | — | — | — | |
| Baseline++Backbone=RN18, Approach=transductive feature extraction2022.08 | 62.04 | — | 42.85 | — | |
| BASELINE++Backbone=ResNet-182021.01 | 62.04 | — | 42.85 | — | |
| PROTONETBackbone=ResNet-182021.01 | 62.02 | — | — | — | |
| SIMPLESHOT†Backbone=Conv-42021.01 | 61.44 | — | 45.36 | — | |
| SIBBackbone=WRN-28-10, Training Paradigm=Supervised, Evaluation Protocol=5-way2022.10 | 59.94 | — | 43.27 | — | |
| BECLR2024.02 | 59.51 | — | 43.45 | — | |
| RelationNet+FTBackbone=ResNet-102021.06 | 59.46 | — | 44.07 | — | |
| Deep Laplacian eigenmapsBackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 58.86 | — | 41.08 | — | |
| Laplacian Eigenmaps2024.02 | 58.86 | — | 41.08 | — | |
| HMS2024.02 | 58.32 | — | 40.75 | — | |
| RELATIONNETBackbone=ResNet-182021.01 | 57.71 | — | — | — | |
| PsCo2024.02 | 57.38 | — | — | — | |
| OVE(PL)Backbone=Conv4, Approach=inductive inference2022.08 | 57.23 | — | 37.49 | — | |
| Barlow TwinsBackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 57.16 | — | 40.46 | — | |
| Barlow Twins2024.02 | 57.16 | — | 40.46 | — | |
| BYOLBackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 56.92 | — | 40.63 | — | |
| BYOL2024.02 | 56.92 | — | 40.63 | — | |
| MTLBackbone=WRN-28-10, Training Paradigm=Supervised, Evaluation Protocol=5-way2022.10 | 56.89 | — | 43.15 | — | |
| LEOBackbone=WRN-28-10, Training Paradigm=Supervised, Evaluation Protocol=5-way2022.10 | 56.66 | — | 41.45 | — | |
| Baseline++Setting=Inductive, Backbone=WRN2020.06 | 56.64 | — | 40.44 | — | |
| MoCo v2Backbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 56.49 | — | 39.29 | — | |
| MoCo v22024.02 | 56.49 | — | 39.29 | — | |
| SimCLRBackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 55.89 | — | 38.25 | — | |
| SimCLR2024.02 | 55.89 | — | 38.25 | — | |
| DKT+CosBackbone=Conv4, Approach=inductive inference2022.08 | 55.65 | — | 40.22 | — | |
| Meta-GMVAEBackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 55.65 | — | 38.09 | — | |
| Meta-GMVAE2024.02 | 55.65 | — | 38.09 | — | |
| ProtoNetBackbone=ResNet-122021.06 | 55.29 | — | 40.05 | — | |
| MatchingNet+FTBackbone=ResNet-102021.06 | 55.23 | — | 36.61 | — | |
| MAMLBackbone=WRN-28-10, Training Paradigm=Supervised, Evaluation Protocol=5-way2022.10 | 55.04 | — | 39.06 | — | |
| NNCLRreproduced=true2024.02 | 54.78 | — | 39.37 | — | |
| SwAVreproduced=true2024.02 | 53.94 | — | 38.34 | — | |
| Matching NetBackbone=WRN-28-10, Training Paradigm=Supervised, Evaluation Protocol=5-way2022.10 | 53.08 | — | 42.04 | — | |
| MAMLBackbone=ResNet-182021.01 | 51.34 | — | — | — | |
| UMTRABackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 50.21 | — | 33.59 | — | |
| CACTUS-MAMLBackbone=WRN-28-10, Training Paradigm=Unsupervised, Evaluation Protocol=5-way2022.10 | 49.97 | — | 33.48 | — | |
| MAMLBackbone=Conv4, Approach=inductive inference2022.08 | 48.83 | — | 34.01 | — | |
| ABMLBackbone=Conv4, Approach=inductive inference2022.08 | 47.8 | — | 31.51 | — | |
| ANILShots=1-shot, Ways=5-way, Backbone=ResNet-122022.10 | — | 37.3 | — | — | |
| ANILShots=5-shot, Ways=5-way, Backbone=ResNet-122022.10 | — | 53.42 | — | — | |
| ANIL + SiMTShots=1-shot, Ways=5-way, Backbone=ResNet-122022.10 | — | 38.86 | — | — | |
| ANIL + SiMTShots=5-shot, Ways=5-way, Backbone=ResNet-122022.10 | — | 56.03 | — | — | |
| Baselineshots=5-shot, backbone=ResNet-182020.03 | — | 65.57 | — | — | |
| BaselineBackbone=ResNet-18, Shot=5-shot2020.08 | — | 65.6 | — | — | |
| BaselineBackbone=ResNet-18, Shots=5-shot2023.04 | — | 65.57 | — | — | |
| Baseline++shots=5-shot, backbone=ResNet-182020.03 | — | 62.04 | — | — | |
| Baseline++Shots=1-shot, Ways=5-ways, Backbone=Conv-4, Number of runs=32019.10 | — | 39.19 | — | — | |
| Baseline++Backbone=ResNet-18, Shots=5-shot2023.04 | — | 62.04 | — | — | |
| BASELINE++Backbone=Conv-42021.01 | — | — | 39.19 | — | |
| DKTKernel=Linear, Shots=1-shot, Ways=5-ways, Backbone=Conv-4, Number of runs=32019.10 | — | 38.72 | — | — | |
| DKTKernel=CosSim, Shots=1-shot, Ways=5-ways, Backbone=Conv-4, Number of runs=32019.10 | — | 40.22 | — | — | |
| DKTKernel=BNCosSim, Shots=1-shot, Ways=5-ways, Backbone=Conv-4, Number of runs=32019.10 | — | 40.14 | — | — | |
| DKTBackbone=Conv-42021.01 | — | — | 40.22 | — | |
| ESPTBackbone=ResNet-12, Shots=1-shot, Local image representation=true2023.04 | — | 54.14 | — | — | |
| ESPTBackbone=ResNet-12, Shots=5-shot, Local image representation=true2023.04 | — | 74.91 | — | — | |
| FEATBackbone=ResNet-12, Shots=1-shot2023.04 | — | 50.67 | — | — |