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算力平台
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SOTA
少样本图像分类
Few Shot Image Classification On Omniglot 1 2
Few Shot Image Classification On Omniglot 1 2
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
MC2+
99.97
Meta-Curvature
DCN6-E
99.92%
Decoder Choice Network for Meta-Learning
DCN4
99.8%
Decoder Choice Network for Meta-Learning
Relation Net
99.6
Learning to Compare: Relation Network for Few-Shot Learning
iMAML, Hessian-Free
99.50
Meta-Learning with Implicit Gradients
MT-net
99.5
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
MAML++
99.47
How to train your MAML
Hyperbolic ProtoNet
99.0
Hyperbolic Image Embeddings
Prototypical Networks
98.8
Prototypical Networks for Few-shot Learning
MAML
98.7
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
VAMPIRE
98.43
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
adaCNN (DF)
98.42
Rapid Adaptation with Conditionally Shifted Neurons
ConvNet with Memory Module
98.4
Learning to Remember Rare Events
Matching Nets
98.1
Matching Networks for One Shot Learning
Neural Statistician
98.1
Towards a Neural Statistician
APL
97.9
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Reptile + Transduction
97.68
On First-Order Meta-Learning Algorithms
0 of 17 row(s) selected.
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HyperAI
HyperAI超神经
首页
算力平台
文档
资讯
论文
教程
数据集
百科
SOTA
LLM 模型天梯
GPU 天梯
顶会
开源项目
全站搜索
关于
服务条款
隐私政策
中文
HyperAI
HyperAI超神经
Toggle Sidebar
全站搜索…
⌘
K
Command Palette
Search for a command to run...
算力平台
首页
SOTA
少样本图像分类
Few Shot Image Classification On Omniglot 1 2
Few Shot Image Classification On Omniglot 1 2
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
MC2+
99.97
Meta-Curvature
DCN6-E
99.92%
Decoder Choice Network for Meta-Learning
DCN4
99.8%
Decoder Choice Network for Meta-Learning
Relation Net
99.6
Learning to Compare: Relation Network for Few-Shot Learning
iMAML, Hessian-Free
99.50
Meta-Learning with Implicit Gradients
MT-net
99.5
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
MAML++
99.47
How to train your MAML
Hyperbolic ProtoNet
99.0
Hyperbolic Image Embeddings
Prototypical Networks
98.8
Prototypical Networks for Few-shot Learning
MAML
98.7
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
VAMPIRE
98.43
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
adaCNN (DF)
98.42
Rapid Adaptation with Conditionally Shifted Neurons
ConvNet with Memory Module
98.4
Learning to Remember Rare Events
Matching Nets
98.1
Matching Networks for One Shot Learning
Neural Statistician
98.1
Towards a Neural Statistician
APL
97.9
Adaptive Posterior Learning: few-shot learning with a surprise-based memory module
Reptile + Transduction
97.68
On First-Order Meta-Learning Algorithms
0 of 17 row(s) selected.
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Next