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Models/UL-Hopfield (ULH)

UL-Hopfield (ULH)

Reported on 9 benchmarks across 4 tasks · 1 paper · 8 SOTA

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

Computer Vision9 results

Image ClassificationonCIFAR100 5-way (1-shot)
Accuracy· 2018-05-02
89.6
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Image ClassificationonCaltech-256 5-way (1-shot)
Accuracy· 2018-05-02
74.7
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Image ClassificationonCaltech-101
Accuracy· 2018-05-02
91
best: 97.76 (Pre trained wide-resnet-101)
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Image ClassificationonCIFAR-10, 40 Labels
Percentage error· 2018-05-02
16.9
best: 3.51 (SemiOccam)
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Fine-Grained Image ClassificationonCaltech-101
Accuracy· 2018-05-02
91
best: 97.76 (Pre trained wide-resnet-101)
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Few-Shot Image ClassificationonCIFAR100 5-way (1-shot)
Accuracy· 2018-05-02
89.6
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Few-Shot Image ClassificationonCaltech-256 5-way (1-shot)
Accuracy· 2018-05-02
74.7
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Semi-Supervised Image ClassificationonCIFAR-10, 40 Labels
Percentage error· 2018-05-02
16.9
best: 3.51 (SemiOccam)
SOTA
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033
Image ClassificationonCIFAR-10
Percentage correct· 2018-05-02
83.1
best: 99.5 (ViT-H/14)
Unsupervised Learning using Pretrained CNN and Associative Memory Bank arXiv:1805.01033