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Few Shot Image Classification On Mini 5

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
MatchingNet (Vinyals et al., 2016)45.59Matching Networks for One Shot Learning
PT+MAP62.49Leveraging the Feature Distribution in Transfer-based Few-Shot Learning
DKT + CosSim40.22Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
MAML (Finn et al., 2017)40.15Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Baseline++ (Chen et al., 2019)33.04A Closer Look at Few-shot Classification
PEMnE-BMS*63.90Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
HyperShot40.03HyperShot: Few-Shot Learning by Kernel HyperNetworks
FEAT (Ye et al., 2018)39.00Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
DAPNA49.44Few-Shot Learning as Domain Adaptation: Algorithm and Analysis-
TRIDENT84.61Transductive Decoupled Variational Inference for Few-Shot Classification
RelationNet (Sung et al., 2018)42.91Learning to Compare: Relation Network for Few-Shot Learning
ProtoNet (Snell et al., 2017)45.31Prototypical Networks for Few-shot Learning
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Few Shot Image Classification On Mini 5 | SOTA | HyperAI