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Few Shot Image Classification On Mini 13

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
Transductive CNAPS + FETI	85.9	Enhancing Few-Shot Image Classification with Unlabelled Examples
Simple CNAPS + FETI	83.1	Improved Few-Shot Visual Classification
TIM-GD	72.8	Transductive Information Maximization For Few-Shot Learning
Transductive CNAPS	59.6	Enhancing Few-Shot Image Classification with Unlabelled Examples
Simple CNAPS	56.7	Improved Few-Shot Visual Classification
TPN (Higher Shot)	52.8	Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Label Propagation	51.2	Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Prototypical Networks (Higher Way)	50.1	Prototypical Networks for Few-shot Learning
Prototypical Networks	49.3	Prototypical Networks for Few-shot Learning
MAML + Transduction	48.2	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Relation Networks	47.9	Learning to Compare: Relation Network for Few-Shot Learning
Reptile+BN	47.6	On First-Order Meta-Learning Algorithms
MAML	46.9	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Reptile	44.7	On First-Order Meta-Learning Algorithms

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Few Shot Image Classification On Mini 13 | SOTA | HyperAI