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5-way Few-shot Image Classification on tieredImageNet 5-shot (test)

87.13AccuracynoHub-S
54.234862.774971.31579.8551Mar 27, 2019Nov 23, 2019Jul 22, 2020Mar 21, 2021Nov 17, 2021Jul 17, 2022Mar 16, 2023

Evaluation results

MethodLinks
noHub-SBackbone=ResNet182023.0387.136.67
noHubBackbone=WideRes28-102023.0387.075.5
noHub-SBackbone=WideRes28-102023.0387.055.83
noHubBackbone=ResNet182023.0386.94.5
TCPRBackbone=ResNet182023.0386.543.83
Robust 20 FullInput size=224, Network=ResNet2019.0386.49—
CL2Backbone=ResNet182023.0386.433
EASEBackbone=ResNet182023.0386.43.17
TCPRBackbone=WideRes28-102023.0386.374
L2Backbone=WideRes28-102023.0386.293.83
EASEBackbone=WideRes28-102023.0386.293.33
L2Backbone=ResNet182023.0386.051.83
CL2Backbone=WideRes28-102023.0385.471.5
Robust 20-distInput size=224, Network=ResNet2019.0385.43—
NoneBackbone=WideRes28-102023.0384.11.67
Mean CentroidInput size=224, Network=ResNet2019.0383.89—
TADAMInput size=84, Network=ResNet2019.0381.92—
MetaOptNet-SVM-trainvalbackbone=ResNet-12, training_set=meta-training + meta-validation2019.0481.75—
MetaOptNet-SVMbackbone=ResNet-122019.0481.56—
ReRepBackbone=WideRes28-102023.0381.511.67
LEObackbone=WRN-28-102019.0481.44—
LEOInput size=80, Network=WideResNet2019.0381.44—
MetaOptNet-RRbackbone=ResNet-122019.0481.34—
ZNBackbone=WideRes28-102023.0375.345
ZNBackbone=ResNet182023.0375.323.83
ReRepBackbone=ResNet182023.0374.521.83
NoneBackbone=ResNet182023.0374.380.67
HTChannels=642022.0173.9—
Transductive Prop Netsbackbone=64-64-64-642019.0473.3—
RFSChannels=642022.0173.2—
Prototypical Networksbackbone=64-64-64-642019.0472.69—
Relation Networksbackbone=64-96-128-2562019.0471.32—
HTChannels=482022.0171.1—
MAMLbackbone=32-32-32-322019.0470.3—
HTChannels=322022.0170.2—
RFSChannels=482022.0169.3—
RFSChannels=322022.0166.3—
HTChannels=162022.0165.8—
RFSChannels=162022.0162—
HTChannels=82022.0161.9—
RFSChannels=82022.0155.5—