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Few Shot Image Classification On Omniglot 5 2

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Accuracy	Paper Title	Repository
Relation Net	99.8	Learning to Compare: Relation Network for Few-Shot Learning
Prototypical Networks	99.7	Prototypical Networks for Few-shot Learning
VAMPIRE	99.56%	Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
MAML++	99.85%	How to train your MAML
MC2+	99.89	Meta-Curvature
adaCNN (DF)	99.37	Rapid Adaptation with Conditionally Shifted Neurons	-
iMAML, Hessian-Free	99.74%	Meta-Learning with Implicit Gradients
Neural Statistician	99.5	Towards a Neural Statistician
DCN6-E	99.92%	Decoder Choice Network for Meta-Learning
DCN4	99.89%	Decoder Choice Network for Meta-Learning
Hyperbolic ProtoNet	99.4	Hyperbolic Image Embeddings
Matching Nets	98.9	Matching Networks for One Shot Learning
ConvNet with Memory Module	99.6	Learning to Remember Rare Events
MAML	99.9	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
APL	99.9	Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Reptile + Transduction	99.48	On First-Order Meta-Learning Algorithms

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Few Shot Image Classification On Omniglot 5 2 | SOTA | HyperAI