Few-Shot Image Classification on Stanford Cars 5-way (5-shot)

Metric: Accuracy (higher is better)

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#	Model↕	Accuracy▼	Extra Data	Paper	Date↕	Code
1	MATANet	91.89	No	Multi-scale Adaptive Task Attention Network for ...	2020-11-30	-
2	MML(KL)	91.05	No	Multi-level Metric Learning for Few-shot Image R...	2021-03-21	-
3	DN4-DA (k=1)	89.6	No	Revisiting Local Descriptor based Image-to-Class...	2019-03-28	Code
4	GNN++	71.25	No	Few-Shot Learning with Graph Neural Networks	2017-11-10	Code
5	Prototypical Nets++	52.93	No	Prototypical Networks for Few-shot Learning	2017-03-15	Code
6	Matching Nets FCE++	44.7	No	Matching Networks for One Shot Learning	2016-06-13	Code

#1MATANetSOTA
91.89
Accuracy· 2020-11-30
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
#2MML(KL)
91.05
Accuracy· 2021-03-21
Multi-level Metric Learning for Few-shot Image Recognition
#3DN4-DA (k=1)SOTA
89.6
Accuracy· 2019-03-28
Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning Code
#4GNN++SOTA
71.25
Accuracy· 2017-11-10
Few-Shot Learning with Graph Neural Networks Code
#5Prototypical Nets++SOTA
52.93
Accuracy· 2017-03-15
Prototypical Networks for Few-shot Learning Code
#6Matching Nets FCE++SOTA
44.7
Accuracy· 2016-06-13
Matching Networks for One Shot Learning Code