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/Self-Critique and Adapt + High-End MAML++

Self-Critique and Adapt + High-End MAML++

Reported on 8 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Computer Vision8 results

Image ClassificationonCUB 200 5-way 5-shot
Accuracy· 2019-05-24
85.63
best: 98.7 (CAML [Laion-2b])
SOTA
Learning to learn via Self-Critique arXiv:1905.10295
Image ClassificationonCUB 200 5-way 1-shot
Accuracy· 2019-05-24
70.46
best: 95.8 (PT+MAP+SF+SOT (transductive))
SOTA
Learning to learn via Self-Critique arXiv:1905.10295
Few-Shot Image ClassificationonCUB 200 5-way 5-shot
Accuracy· 2019-05-24
85.63
best: 98.7 (CAML [Laion-2b])
SOTA
Learning to learn via Self-Critique arXiv:1905.10295
Few-Shot Image ClassificationonCUB 200 5-way 1-shot
Accuracy· 2019-05-24
70.46
best: 95.8 (PT+MAP+SF+SOT (transductive))
SOTA
Learning to learn via Self-Critique arXiv:1905.10295
Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2019-05-24
77.64
best: 98.72 (SgVA-CLIP)
Learning to learn via Self-Critique arXiv:1905.10295
Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2019-05-24
62.86
best: 97.95 (SgVA-CLIP)
Learning to learn via Self-Critique arXiv:1905.10295
Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
Accuracy· 2019-05-24
77.64
best: 98.72 (SgVA-CLIP)
Learning to learn via Self-Critique arXiv:1905.10295
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2019-05-24
62.86
best: 97.95 (SgVA-CLIP)
Learning to learn via Self-Critique arXiv:1905.10295