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Models/Matching Nets FCE++

Matching Nets FCE++

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision6 results

Image ClassificationonStanford Dogs 5-way (5-shot)
Accuracy· 2016-06-13
47.5
best: 75.59 (MML(KL))
SOTA
Matching Networks for One Shot Learning arXiv:1606.04080
Image ClassificationonStanford Cars 5-way (5-shot)
Accuracy· 2016-06-13
44.7
best: 91.89 (MATANet)
SOTA
Matching Networks for One Shot Learning arXiv:1606.04080
Image ClassificationonStanford Cars 5-way (1-shot)
Accuracy· 2016-06-13
34.8
best: 73.15 (MATANet)
SOTA
Matching Networks for One Shot Learning arXiv:1606.04080
Few-Shot Image ClassificationonStanford Dogs 5-way (5-shot)
Accuracy· 2016-06-13
47.5
best: 75.59 (MML(KL))
SOTA
Matching Networks for One Shot Learning arXiv:1606.04080
Few-Shot Image ClassificationonStanford Cars 5-way (5-shot)
Accuracy· 2016-06-13
44.7
best: 91.89 (MATANet)
SOTA
Matching Networks for One Shot Learning arXiv:1606.04080
Few-Shot Image ClassificationonStanford Cars 5-way (1-shot)
Accuracy· 2016-06-13
34.8
best: 73.15 (MATANet)
SOTA
Matching Networks for One Shot Learning arXiv:1606.04080