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Models/Reptile+BN

Reptile+BN

Reported on 8 benchmarks across 2 tasks · 1 paper

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

Computer Vision8 results

Image ClassificationonTiered ImageNet 10-way (1-shot)
Accuracy· 2018-03-08
35.3
best: 65.1 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2018-03-08
47.6
best: 85.9 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2018-03-08
32
best: 68.5 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonTiered ImageNet 10-way (5-shot)
Accuracy· 2018-03-08
52
best: 80.6 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonTiered ImageNet 10-way (1-shot)
Accuracy· 2018-03-08
35.3
best: 65.1 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2018-03-08
47.6
best: 85.9 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2018-03-08
32
best: 68.5 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonTiered ImageNet 10-way (5-shot)
Accuracy· 2018-03-08
52
best: 80.6 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999