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/Prototypical Networks

Prototypical Networks

Reported on 24 benchmarks across 2 tasks · 1 paper · 12 SOTA

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

Computer Vision24 results

Image ClassificationonMeta-Dataset
Accuracy· 2017-03-15
60.573
best: 85.27 (SMAT (DINO-VIT-Base-16-224))
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonOMNIGLOT - 1-Shot, 5-way
Accuracy· 2017-03-15
98.8
best: 99.97 (MC2+)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2017-03-15
68.2
best: 98.72 (SgVA-CLIP)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2017-03-15
49.42
best: 97.95 (SgVA-CLIP)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonCUB 200 50-way (0-shot)
Accuracy· 2017-03-15
54.6
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonMini-Imagenet 5-way (10-shot)
Accuracy· 2017-03-15
74.3
best: 90.03 (PT+MAP)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMeta-Dataset
Accuracy· 2017-03-15
60.573
best: 85.27 (SMAT (DINO-VIT-Base-16-224))
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonOMNIGLOT - 1-Shot, 5-way
Accuracy· 2017-03-15
98.8
best: 99.97 (MC2+)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2017-03-15
68.2
best: 98.72 (SgVA-CLIP)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2017-03-15
49.42
best: 97.95 (SgVA-CLIP)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonCUB 200 50-way (0-shot)
Accuracy· 2017-03-15
54.6
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMini-Imagenet 5-way (10-shot)
Accuracy· 2017-03-15
74.3
best: 90.03 (PT+MAP)
SOTA
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonTiered ImageNet 10-way (1-shot)
Accuracy· 2017-03-15
37.3
best: 65.1 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2017-03-15
49.3
best: 85.9 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonOMNIGLOT - 5-Shot, 5-way
Accuracy· 2017-03-15
99.7
best: 99.9 (MAML)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2017-03-15
32.9
best: 68.5 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonMeta-Dataset Rank
Mean Rank· 2017-03-15
8.5
best: 11.8 (Relation Networks)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Image ClassificationonTiered ImageNet 10-way (5-shot)
Accuracy· 2017-03-15
57.8
best: 80.6 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonTiered ImageNet 10-way (1-shot)
Accuracy· 2017-03-15
37.3
best: 65.1 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2017-03-15
49.3
best: 85.9 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonOMNIGLOT - 5-Shot, 5-way
Accuracy· 2017-03-15
99.7
best: 99.9 (MAML)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2017-03-15
32.9
best: 68.5 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonMeta-Dataset Rank
Mean Rank· 2017-03-15
8.5
best: 11.8 (Relation Networks)
Prototypical Networks for Few-shot Learning arXiv:1703.05175
Few-Shot Image ClassificationonTiered ImageNet 10-way (5-shot)
Accuracy· 2017-03-15
57.8
best: 80.6 (Transductive CNAPS + FETI)
Prototypical Networks for Few-shot Learning arXiv:1703.05175