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/SGM (ResNet-50)

SGM (ResNet-50)

Reported on 6 benchmarks across 2 tasks · 1 paper · 6 SOTA

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

Computer Vision6 results

Image ClassificationonImageNet-FS (5-shot, all)
Top-5 Accuracy (%)· 2016-06-09
77.4
best: 81.12 (KGTN-ens (ResNet-50, h+g, max))
SOTA
Low-shot Visual Recognition by Shrinking and Hallucinating Features arXiv:1606.02819
Image ClassificationonImageNet-FS (1-shot, novel)
Top-5 Accuracy (%)· 2016-06-09
52.9
best: 62.73 (KGTN-ens (ResNet-50, h+g, max))
SOTA
Low-shot Visual Recognition by Shrinking and Hallucinating Features arXiv:1606.02819
Image ClassificationonImageNet-FS (2-shot, novel)
Top-5 Accuracy (%)· 2016-06-09
67
best: 71.48 (KGTN-ens (ResNet-50, h+g, max))
SOTA
Low-shot Visual Recognition by Shrinking and Hallucinating Features arXiv:1606.02819
Few-Shot Image ClassificationonImageNet-FS (5-shot, all)
Top-5 Accuracy (%)· 2016-06-09
77.4
best: 81.12 (KGTN-ens (ResNet-50, h+g, max))
SOTA
Low-shot Visual Recognition by Shrinking and Hallucinating Features arXiv:1606.02819
Few-Shot Image ClassificationonImageNet-FS (1-shot, novel)
Top-5 Accuracy (%)· 2016-06-09
52.9
best: 62.73 (KGTN-ens (ResNet-50, h+g, max))
SOTA
Low-shot Visual Recognition by Shrinking and Hallucinating Features arXiv:1606.02819
Few-Shot Image ClassificationonImageNet-FS (2-shot, novel)
Top-5 Accuracy (%)· 2016-06-09
67
best: 71.48 (KGTN-ens (ResNet-50, h+g, max))
SOTA
Low-shot Visual Recognition by Shrinking and Hallucinating Features arXiv:1606.02819