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Models/DA-VAE

DA-VAE

Reported on 12 benchmarks across 1 task

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

Methodology12 results

  • Generalized Few-Shot LearningonAwA2
    Per-Class Accuracy (1-shot)
    68
    best: 69.9 (MVCN)
  • Generalized Few-Shot LearningonAwA2
    Per-Class Accuracy (10-shots)
    76.8
    best: 82.2 (MVCN)
  • Generalized Few-Shot LearningonAwA2
    Per-Class Accuracy (2-shots)
    73
    best: 76.4 (MVCN)
  • Generalized Few-Shot LearningonAwA2
    Per-Class Accuracy (5-shots)
    75.6
    best: 81.2 (MVCN)
  • Generalized Few-Shot LearningonCUB
    Per-Class Accuracy (2-shots)
    54.6
    best: 61.6 (MVCN)
  • Generalized Few-Shot LearningonCUB
    Per-Class Accuracy (1-shot)
    49.2
    best: 57.3 (MVCN)
  • Generalized Few-Shot LearningonCUB
    Per-Class Accuracy (10-shots)
    60.8
    best: 67.8 (MVCN)
  • Generalized Few-Shot LearningonCUB
    Per-Class Accuracy (5-shots)
    58.8
    best: 65.4 (MVCN)
  • Generalized Few-Shot LearningonSUN
    Per-Class Accuracy (1-shot)
    40.6
    best: 41 (DRAGON)
  • Generalized Few-Shot LearningonSUN
    Per-Class Accuracy (10-shots)
    47.6
    best: 48.2 (DRAGON)
  • Generalized Few-Shot LearningonSUN
    Per-Class Accuracy (2-shots)
    43
    best: 43.8 (DRAGON)
  • Generalized Few-Shot LearningonSUN
    Per-Class Accuracy (5-shots)
    46
    best: 46.7 (DRAGON)