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Few Shot Image Classification On Meta Dataset 1

Métriques

Mean Rank

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	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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Few Shot Image Classification On Meta Dataset 1 | SOTA | HyperAI