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ResearchFormerly WizWand

Few-shot classification on CropDiseases (test)

94.95AccuracyPsCo
55.679665.874876.0786.2652Mar 2, 2023Mar 21, 2023Apr 9, 2023Apr 29, 2023May 18, 2023Jun 6, 2023Jun 26, 2023
Updated 8mo ago

Evaluation results

MethodLinks
PsCoWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0394.95
ATAway=5-way, shot=5-shot2023.0690.59
ProtoDiffway=5-way, shot=5-shot2023.0690.15
MoCo v2Way=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0389.85
SwAVWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0389.24
SimCLRWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0388.73
PsCoWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0388.24
AFAway=5-way, shot=5-shot2023.0688.06
HVMway=5-way, shot=5-shot2023.0687.65
ProtoNetsWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0383.64
MAMLWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0383.24
Meta-SVEBMWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0383.13
GNNway=5-way, shot=5-shot2023.0683.12
Meta-GMVAEWay=5, Shot=20, Backbone=Conv5, Pre-trained=miniImageNet2023.0381.22
MoCo v2Way=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0380.96
SwAVWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0380.15
SimCLRWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0379.9
ProtoNetway=5-way, shot=5-shot2023.0679.72
MAMLWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0377.76
ProtoNetsWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0376.01
Meta-GMVAEWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0373.56
Meta-SVEBMWay=5, Shot=5, Backbone=Conv5, Pre-trained=miniImageNet2023.0371.82
ProtoDiffway=5-way, shot=1-shot2023.0668.93
AFAway=5-way, shot=1-shot2023.0667.61
ATAway=5-way, shot=1-shot2023.0667.47
HVMway=5-way, shot=1-shot2023.0665.13
ProtoNetway=5-way, shot=1-shot2023.0657.57
GNNway=5-way, shot=1-shot2023.0657.19