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Models/MAML++

MAML++

Reported on 8 benchmarks across 2 tasks · 2 papers · 2 SOTA

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

Computer Vision10 results

  • Image ClassificationonOMNIGLOT - 1-Shot, 20-way
    Accuracy· 2018-10-22
    97.65
    best: 99.63 (GCR)
    SOTA
    How to train your MAMLarXiv:1810.09502
  • Few-Shot Image ClassificationonOMNIGLOT - 1-Shot, 20-way
    Accuracy· 2018-10-22
    97.65
    best: 99.63 (GCR)
    SOTA
    How to train your MAMLarXiv:1810.09502
  • Image ClassificationonOMNIGLOT - 1-Shot, 20-way
    Accuracy· 2022-01-11
    97.7
    best: 99.63 (GCR)
    HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot LearningarXiv:2201.04182
  • Few-Shot Image ClassificationonOMNIGLOT - 1-Shot, 20-way
    Accuracy· 2022-01-11
    97.7
    best: 99.63 (GCR)
    HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot LearningarXiv:2201.04182
  • Image ClassificationonOMNIGLOT - 1-Shot, 5-way
    Accuracy· 2018-10-22
    99.47
    best: 99.97 (MC2+)
    How to train your MAMLarXiv:1810.09502
  • Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy· 2018-10-22
    67.15
    best: 98.72 (SgVA-CLIP)
    How to train your MAMLarXiv:1810.09502
  • Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy· 2018-10-22
    52.4
    best: 97.95 (SgVA-CLIP)
    How to train your MAMLarXiv:1810.09502
  • Few-Shot Image ClassificationonOMNIGLOT - 1-Shot, 5-way
    Accuracy· 2018-10-22
    99.47
    best: 99.97 (MC2+)
    How to train your MAMLarXiv:1810.09502
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (5-shot)
    Accuracy· 2018-10-22
    67.15
    best: 98.72 (SgVA-CLIP)
    How to train your MAMLarXiv:1810.09502
  • Few-Shot Image ClassificationonMini-Imagenet 5-way (1-shot)
    Accuracy· 2018-10-22
    52.4
    best: 97.95 (SgVA-CLIP)
    How to train your MAMLarXiv:1810.09502