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Few Shot Image Classification On Mini 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
PT+MAP	82.92%	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
MCT	78.55%	Meta-Learned Confidence for Few-shot Learning
EPNet	77.27%	Embedding Propagation: Smoother Manifold for Few-Shot Classification
AmdimNet	76.82%	Self-Supervised Learning For Few-Shot Image Classification
Transfer+SGC	76.47%	Graph-based Interpolation of Feature Vectors for Accurate Few-Shot Classification
BaseTransformers (Inductive)	70.88%	BaseTransformers: Attention over base data-points for One Shot Learning
BD-CSPN	70.31%	Prototype Rectification for Few-Shot Learning
SIB	70.0%	-	-
Multiple-semantics	67.2%	Baby steps towards few-shot learning with multiple semantics	-
R2-D2+Task Aug	65.95%	Task Augmentation by Rotating for Meta-Learning
MetaOptNet-SVM+Task Aug	65.38%	Task Augmentation by Rotating for Meta-Learning
Neg-Margin	63.85	Negative Margin Matters: Understanding Margin in Few-shot Classification
DivCoop	63.73%	Diversity with Cooperation: Ensemble Methods for Few-Shot Classification
GCR	53.21	Few-Shot Learning with Global Class Representations
HyperShot	53.18%	HyperShot: Few-Shot Learning by Kernel HyperNetworks
PLATIPUS	50.13%	Probabilistic Model-Agnostic Meta-Learning

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Few Shot Image Classification On Mini 1 | SOTA | HyperAI