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Models/Reptile + Transduction

Reptile + Transduction

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 ClassificationonOMNIGLOT - 1-Shot, 5-way
Accuracy· 2018-03-08
97.68
best: 99.97 (MC2+)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonOMNIGLOT - 5-Shot, 5-way
Accuracy· 2018-03-08
99.48
best: 99.9 (MAML)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2018-03-08
65.99
best: 98.72 (SgVA-CLIP)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2018-03-08
49.97
best: 97.95 (SgVA-CLIP)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonOMNIGLOT - 1-Shot, 5-way
Accuracy· 2018-03-08
97.68
best: 99.97 (MC2+)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonOMNIGLOT - 5-Shot, 5-way
Accuracy· 2018-03-08
99.48
best: 99.9 (MAML)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2018-03-08
65.99
best: 98.72 (SgVA-CLIP)
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2018-03-08
49.97
best: 97.95 (SgVA-CLIP)
On First-Order Meta-Learning Algorithms arXiv:1803.02999