Few Shot Image Classification On Meta Dataset 1

Metrics

Mean Rank

Results

Performance results of various models on this benchmark

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

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Few Shot Image Classification On Meta Dataset 1

Metrics

Mean Rank

Results

Performance results of various models on this benchmark

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

0 of 13 row(s) selected.