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Few Shot Image Classification On Mini 5

Metrics

Accuracy

Results

Performance results of various models on this benchmark

		Paper Title	Repository
TRIDENT	84.61	Transductive Decoupled Variational Inference for Few-Shot Classification
PEMnE-BMS*	63.90	Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
PT+MAP	62.49	Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
DAPNA	49.44	Few-Shot Learning as Domain Adaptation: Algorithm and Analysis	-
MatchingNet (Vinyals et al., 2016)	45.59	Matching Networks for One Shot Learning
ProtoNet (Snell et al., 2017)	45.31	Prototypical Networks for Few-shot Learning
RelationNet (Sung et al., 2018)	42.91	Learning to Compare: Relation Network for Few-Shot Learning
DKT + CosSim	40.22	Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
MAML (Finn et al., 2017)	40.15	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
HyperShot	40.03	HyperShot: Few-Shot Learning by Kernel HyperNetworks
FEAT (Ye et al., 2018)	39.00	Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
Baseline++ (Chen et al., 2019)	33.04	A Closer Look at Few-shot Classification

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Few Shot Image Classification On Mini 5 | SOTA | HyperAI