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SOTA
少样本图像分类
Few Shot Image Classification On Omniglot 5 1
Few Shot Image Classification On Omniglot 5 1
评估指标
Accuracy
评测结果
各个模型在此基准测试上的表现结果
Columns
模型名称
Accuracy
Paper Title
Repository
MC2+
99.65%
Meta-Curvature
DCN6-E
99.63
Decoder Choice Network for Meta-Learning
DCN4
99.5%
Decoder Choice Network for Meta-Learning
TapNet
99.49%
TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning
MAML++
99.33%
How to train your MAML
GCR
99.32
Few-Shot Learning with Global Class Representations
MAML++
99.3%
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learning
iMAML, Hessian-Free
99.14%
Meta-Learning with Implicit Gradients
Relation Net
99.1%
Learning to Compare: Relation Network for Few-Shot Learning
Prototypical Networks
98.9%
Prototypical Networks for Few-shot Learning
ConvNet with Memory Module
98.6%
Learning to Remember Rare Events
VAMPIRE
98.52%
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
Matching Nets
98.5%
Matching Networks for One Shot Learning
adaCNN (DF)
98.43%
Rapid Adaptation with Conditionally Shifted Neurons
-
Hyperbolic ProtoNet
98.15%
Hyperbolic Image Embeddings
Neural Statistician
98.1%
Towards a Neural Statistician
APL
97.6%
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Reptile + Transduction
97.12%
On First-Order Meta-Learning Algorithms
MR-MAML
94.1%
Meta-Learning without Memorization
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