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Few Shot Image Classification On Omniglot 5 1

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Accuracy	Paper Title	Repository
Prototypical Networks	98.9%	Prototypical Networks for Few-shot Learning
GCR	99.32	Few-Shot Learning with Global Class Representations
VAMPIRE	98.52%	Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
ConvNet with Memory Module	98.6%	Learning to Remember Rare Events
Relation Net	99.1%	Learning to Compare: Relation Network for Few-Shot Learning
Matching Nets	98.5%	Matching Networks for One Shot Learning
DCN6-E	99.63	Decoder Choice Network for Meta-Learning
Hyperbolic ProtoNet	98.15%	Hyperbolic Image Embeddings
DCN4	99.5%	Decoder Choice Network for Meta-Learning
Reptile + Transduction	97.12%	On First-Order Meta-Learning Algorithms
MAML++	99.3%	HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
MR-MAML	94.1%	Meta-Learning without Memorization
adaCNN (DF)	98.43%	Rapid Adaptation with Conditionally Shifted Neurons	-
TapNet	99.49%	TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
MC2+	99.65%	Meta-Curvature
Neural Statistician	98.1%	Towards a Neural Statistician
APL	97.6%	Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
MAML++	99.33%	How to train your MAML
iMAML, Hessian-Free	99.14%	Meta-Learning with Implicit Gradients

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