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/MTL

MTL

Reported on 12 benchmarks across 3 tasks · 2 papers · 6 SOTA

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

Computer Vision10 results

Image ClassificationonFC100 5-way (10-shot)
Accuracy· 2018-12-06
63.4
SOTA
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Image ClassificationonFC100 5-way (5-shot)
Accuracy· 2018-12-06
57.6
best: 70.6 (BAVARDAGE)
SOTA
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Image ClassificationonFC100 5-way (1-shot)
Accuracy· 2018-12-06
45.1
best: 57.27 (BAVARDAGE)
SOTA
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Few-Shot Image ClassificationonFC100 5-way (10-shot)
Accuracy· 2018-12-06
63.4
SOTA
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Few-Shot Image ClassificationonFC100 5-way (5-shot)
Accuracy· 2018-12-06
57.6
best: 70.6 (BAVARDAGE)
SOTA
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Few-Shot Image ClassificationonFC100 5-way (1-shot)
Accuracy· 2018-12-06
45.1
best: 57.27 (BAVARDAGE)
SOTA
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2018-12-06
75.5
best: 98.72 (SgVA-CLIP)
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2018-12-06
61.2
best: 97.95 (SgVA-CLIP)
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2018-12-06
75.5
best: 98.72 (SgVA-CLIP)
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2018-12-06
61.2
best: 97.95 (SgVA-CLIP)
Meta-Transfer Learning for Few-Shot Learning arXiv:1812.02391

Methodology2 results

Anomaly DetectiononUnlabeled CIFAR-10 vs CIFAR-100
AUROC· 2021-06-07
82.92
best: 96.7 (PsudoLabels ViT)
Shifting Transformation Learning for Out-of-Distribution Detection arXiv:2106.03899
Anomaly DetectiononOne-class CIFAR-100
AUROC· 2021-06-07
83.95
best: 98.4 (GeneralAD)
Shifting Transformation Learning for Out-of-Distribution Detection arXiv:2106.03899