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Few Shot Image Classification On Mini 1

評価指標

Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
Accuracy
Paper TitleRepository
MCT78.55%Meta-Learned Confidence for Few-shot Learning-
BD-CSPN70.31%Prototype Rectification for Few-Shot Learning-
HyperShot53.18%HyperShot: Few-Shot Learning by Kernel HyperNetworks-
R2-D2+Task Aug65.95%Task Augmentation by Rotating for Meta-Learning-
AmdimNet76.82%Self-Supervised Learning For Few-Shot Image Classification-
MetaOptNet-SVM+Task Aug65.38%Task Augmentation by Rotating for Meta-Learning-
GCR53.21Few-Shot Learning with Global Class Representations-
Neg-Margin63.85Negative Margin Matters: Understanding Margin in Few-shot Classification-
EPNet77.27%Embedding Propagation: Smoother Manifold for Few-Shot Classification-
PLATIPUS50.13%Probabilistic Model-Agnostic Meta-Learning-
BaseTransformers (Inductive)70.88%BaseTransformers: Attention over base data-points for One Shot Learning-
SIB70.0%--
Multiple-semantics67.2%Baby steps towards few-shot learning with multiple semantics-
PT+MAP82.92%Leveraging the Feature Distribution in Transfer-based Few-Shot Learning-
DivCoop63.73%Diversity with Cooperation: Ensemble Methods for Few-Shot Classification-
Transfer+SGC76.47%Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification-
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