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Models/CGEM (ResNet-34)

CGEM (ResNet-34)

Reported on 20 benchmarks across 2 tasks

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

Computer Vision20 results

  • Image ClassificationonaPY
    Concept Accuracy (%)
    71.19
  • Image ClassificationonaPY
    Task Accuracy (%)
    43.75
  • Image ClassificationonCUB-200-2011
    Concept Accuracy (%)
    90.82
    best: 96.58 (EQ-CBM (ResNet-34))
  • Image ClassificationonCUB-200-2011
    Task Accuracy (%)
    79.68
  • Image ClassificationonCelebA
    Concept Accuracy (%)
    86.44
    best: 90.617 (EQ-CBM (ResNet-34))
  • Image ClassificationonCelebA
    Task Accuracy (%)
    42.1
    best: 56.6 (EQ-CBM (ResNet-34))
  • Image ClassificationonAwA2
    Concept Accuracy (%)
    93.68
    best: 99.129 (EQ-CBM (ResNet-34))
  • Image ClassificationonAwA2
    Task Accuracy (%)
    94.63
    best: 95.965 (EQ-CBM (ResNet-34))
  • Image ClassificationonCheXpert
    Concept Accuracy (%)
    63.52
  • Image ClassificationonCheXpert
    Task Accuracy (%)
    89.25
  • Concept-based ClassificationonaPY
    Concept Accuracy (%)
    71.19
  • Concept-based ClassificationonaPY
    Task Accuracy (%)
    43.75
  • Concept-based ClassificationonCUB-200-2011
    Concept Accuracy (%)
    90.82
    best: 96.58 (EQ-CBM (ResNet-34))
  • Concept-based ClassificationonCUB-200-2011
    Task Accuracy (%)
    79.68
  • Concept-based ClassificationonCelebA
    Concept Accuracy (%)
    86.44
    best: 90.617 (EQ-CBM (ResNet-34))
  • Concept-based ClassificationonCelebA
    Task Accuracy (%)
    42.1
    best: 56.6 (EQ-CBM (ResNet-34))
  • Concept-based ClassificationonAwA2
    Concept Accuracy (%)
    93.68
    best: 99.129 (EQ-CBM (ResNet-34))
  • Concept-based ClassificationonAwA2
    Task Accuracy (%)
    94.63
    best: 95.965 (EQ-CBM (ResNet-34))
  • Concept-based ClassificationonCheXpert
    Concept Accuracy (%)
    63.52
  • Concept-based ClassificationonCheXpert
    Task Accuracy (%)
    89.25