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Few Shot Image Classification On Tiered 2

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
MAML34.4Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Reptile+BN35.3On First-Order Meta-Learning Algorithms
MAML + Transduction34.8Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Reptile33.7On First-Order Meta-Learning Algorithms
TPN (Higher Shot)44.8Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Transductive CNAPS54.6Enhancing Few-Shot Image Classification with Unlabelled Examples
Simple CNAPS + FETI57.1Improved Few-Shot Visual Classification
Prototypical Networks (Higher Way)38.6Prototypical Networks for Few-shot Learning
Prototypical Networks37.3Prototypical Networks for Few-shot Learning
Simple CNAPS48.1Improved Few-Shot Visual Classification
Label Propagation39.4Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Relation Networks36.3Learning to Compare: Relation Network for Few-Shot Learning
Transductive CNAPS + FETI65.1Enhancing Few-Shot Image Classification with Unlabelled Examples
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Few Shot Image Classification On Tiered 2 | SOTA | HyperAI