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/TIM-GD

TIM-GD

Reported on 18 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 Vision18 results

Image ClassificationonMini-Imagenet 20-way (1-shot)
Accuracy· 2020-08-25
39.3
SOTA
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonMini-Imagenet 20-way (5-shot)
Accuracy· 2020-08-25
59.5
SOTA
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonMini-ImageNet to CUB - 5 shot learning
Accuracy· 2020-08-25
71
SOTA
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonMini-Imagenet 20-way (1-shot)
Accuracy· 2020-08-25
39.3
SOTA
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonMini-Imagenet 20-way (5-shot)
Accuracy· 2020-08-25
59.5
SOTA
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonMini-ImageNet to CUB - 5 shot learning
Accuracy· 2020-08-25
71
SOTA
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonCUB 200 5-way 5-shot
Accuracy· 2020-08-25
90.8
best: 98.7 (CAML [Laion-2b])
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2020-08-25
72.8
best: 85.9 (Transductive CNAPS + FETI)
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2020-08-25
77.8
best: 97.95 (SgVA-CLIP)
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2020-08-25
56.1
best: 68.5 (Transductive CNAPS + FETI)
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2020-08-25
82.1
best: 96.8 (CAML [Laion-2b])
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2020-08-25
89.8
best: 98.8 (CAML [Laion-2b])
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonCUB 200 5-way 5-shot
Accuracy· 2020-08-25
90.8
best: 98.7 (CAML [Laion-2b])
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonMini-Imagenet 10-way (5-shot)
Accuracy· 2020-08-25
72.8
best: 85.9 (Transductive CNAPS + FETI)
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
Accuracy· 2020-08-25
77.8
best: 97.95 (SgVA-CLIP)
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonMini-Imagenet 10-way (1-shot)
Accuracy· 2020-08-25
56.1
best: 68.5 (Transductive CNAPS + FETI)
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonTiered ImageNet 5-way (1-shot)
Accuracy· 2020-08-25
82.1
best: 96.8 (CAML [Laion-2b])
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297
Few-Shot Image ClassificationonTiered ImageNet 5-way (5-shot)
Accuracy· 2020-08-25
89.8
best: 98.8 (CAML [Laion-2b])
Transductive Information Maximization For Few-Shot Learning arXiv:2008.11297