Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Reptile

Reptile

Reported on 12 benchmarks across 5 tasks · 2 papers · 3 SOTA

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

Computer Vision9 results

Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2018-03-08
66.47
best: 98.8 (CAML [Laion-2b])
On First-Order Meta-Learning Algorithms arXiv:1803.02999
Image ClassificationonTiered ImageNet 10-way (1-shot)
Accuracy· 2018-03-08
33.7
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
44.7
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
31.1
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
48
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
33.7
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
44.7
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
31.1
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
48
best: 80.6 (Transductive CNAPS + FETI)
On First-Order Meta-Learning Algorithms arXiv:1803.02999

Speech2 results

DialogueonFluent Speech Commands
Accuracy (%)· uses extra data· 2020-08-05
99.2
best: 99.8 (Finstreder (Conformer + AMT, character-based))
SOTA
Improving End-to-End Speech-to-Intent Classification with Reptile arXiv:2008.01994
Spoken Language UnderstandingonFluent Speech Commands
Accuracy (%)· uses extra data· 2020-08-05
99.2
best: 99.8 (Finstreder (Conformer + AMT, character-based))
SOTA
Improving End-to-End Speech-to-Intent Classification with Reptile arXiv:2008.01994

Natural Language Processing1 result

Dialogue UnderstandingonFluent Speech Commands
Accuracy (%)· uses extra data· 2020-08-05
99.2
best: 99.8 (Finstreder (Conformer + AMT, character-based))
SOTA
Improving End-to-End Speech-to-Intent Classification with Reptile arXiv:2008.01994