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Models/f-AnoGAN

f-AnoGAN

Reported on 10 benchmarks across 3 tasks

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

Methodology8 results

  • Anomaly DetectiononMVTec LOCO AD
    Avg. Detection AUROC
    64.2
    best: 95.3 (CSAD)
  • Anomaly DetectiononMVTec LOCO AD
    Detection AUROC (only logical)
    65.8
    best: 98.1 (PSAD)
  • Anomaly DetectiononMVTec LOCO AD
    Detection AUROC (only structural)
    62.7
    best: 95.9 (PUAD-M)
  • Anomaly DetectiononMVTec LOCO AD
    Segmentation AU-sPRO (until FPR 5%)
    33.4
    best: 83.2 (SAM-LAD)
  • Anomaly DetectiononGoodsAD
    AUPR
    66.6
    best: 86.1 (PatchCore-100%)
  • Anomaly DetectiononGoodsAD
    AUROC
    62.8
    best: 86.1 (MiniMaxAD-fr)
  • 2D ClassificationonGoodsAD
    AUPR
    66.6
    best: 86.1 (PatchCore-100%)
  • 2D ClassificationonGoodsAD
    AUROC
    62.8
    best: 86.1 (MiniMaxAD-fr)

Computer Vision2 results

  • Anomaly ClassificationonGoodsAD
    AUPR
    66.6
    best: 86.1 (PatchCore-100%)
  • Anomaly ClassificationonGoodsAD
    AUROC
    62.8
    best: 86.1 (MiniMaxAD-fr)