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Models/MAML + Transduction

MAML + Transduction

Reported on 8 benchmarks across 2 tasks · 1 paper · 8 SOTA

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· 2017-03-09
34.8
best: 65.1 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2017-03-09
48.2
best: 85.9 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2017-03-09
31.8
best: 68.5 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Image ClassificationonTiered ImageNet 10-way (5-shot)
Accuracy· 2017-03-09
54.7
best: 80.6 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Few-Shot Image ClassificationonTiered ImageNet 10-way (1-shot)
Accuracy· 2017-03-09
34.8
best: 65.1 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Few-Shot Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2017-03-09
48.2
best: 85.9 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Few-Shot Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2017-03-09
31.8
best: 68.5 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400
Few-Shot Image ClassificationonTiered ImageNet 10-way (5-shot)
Accuracy· 2017-03-09
54.7
best: 80.6 (Transductive CNAPS + FETI)
SOTA
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400