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Few Shot Image Classification On Meta Dataset 1

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Mean Rank

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Mean Rank	Paper Title	Repository
SUR-pnf	4.25	Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification	-
Transductive CNAPS	3.05	Enhancing Few-Shot Image Classification with Unlabelled Examples	-
fo-MAML	10.25	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	-
URT	2.85	A Universal Representation Transformer Layer for Few-Shot Image Classification	-
SUR	4.2	Selecting Relevant Features from a Multi-domain Representation for Few-shot Classification	-
Prototypical Networks	8.5	Prototypical Networks for Few-shot Learning	-
CNAPs	5.95	Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes	-
Matching Networks	10.5	Matching Networks for One Shot Learning	-
Simple CNAPS	3.45	Improved Few-Shot Visual Classification	-
k-NN	10.85	Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples	-
fo-Proto-MAML	6.65	Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples	-
Relation Networks	11.8	Learning to Compare: Relation Network for Few-Shot Learning	-
Finetune	8.7	Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples	-

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