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Models/DAGMM

DAGMM

Reported on 39 benchmarks across 9 tasks · 2 papers

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

Computer Vision25 results

  • Unsupervised Anomaly Detection with Specified Settings -- 20% anomalyonCats and Dogs
    AUC-ROC
    0.914
    best: 0.953 (Shell-Renormalized)
  • Unsupervised Anomaly Detection with Specified Settings -- 20% anomalyoncifar10
    AUC-ROC
    0.883
    best: 0.896 (Shell-Renormalized)
  • Unsupervised Anomaly Detection with Specified Settings -- 20% anomalyonMNIST
    AUC-ROC
    0.616
    best: 0.923 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 20% anomalyonFashion-MNIST
    AUC-ROC
    0.78
    best: 0.889 (IF)
  • Unsupervised Anomaly Detection with Specified Settings -- 20% anomalyonSTL-10
    AUC-ROC
    0.911
    best: 0.999 (Shell-Renormalized)
  • Unsupervised Anomaly Detection with Specified Settings -- 30% anomalyonASSIRA Cat Vs Dog
    AUC-ROC
    0.846
  • Unsupervised Anomaly Detection with Specified Settings -- 30% anomalyonSTL-10
    AUC-ROC
    0.883
    best: 0.999 (Shell-Renormalized)
  • Unsupervised Anomaly Detection with Specified Settings -- 30% anomalyonCIFAR-10
    AUC-ROC
    0.85
    best: 0.894 (Shell-Renormalized)
  • Unsupervised Anomaly Detection with Specified Settings -- 30% anomalyonFashion-MNIST
    AUC-ROC
    0.769
    best: 0.889 (IF)
  • Unsupervised Anomaly Detection with Specified Settings -- 30% anomalyonMNIST
    AUC-ROC
    0.613
    best: 0.904 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 1% anomalyonSTL-10
    AUC-ROC
    0.477
    best: 0.993 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 1% anomalyonMNIST
    AUC-ROC
    0.708
    best: 0.948 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 1% anomalyonCIFAR-10
    AUC-ROC
    0.503
    best: 0.94 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 1% anomalyonCats and Dogs
    AUC-ROC
    0.71
    best: 0.981 (RSRAE)
  • Unsupervised Anomaly Detection with Specified Settings -- 1% anomalyonFashion-MNIST
    AUC-ROC
    0.793
    best: 0.917 (Isolation Forest)
  • Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyonMNIST
    AUC-ROC
    0.624
    best: 0.974 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyonSTL-10
    AUC-ROC
    0.574
    best: 0.998 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyonCIFAR-10
    AUC-ROC
    0.494
    best: 0.93 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyonCats and Dogs
    AUC-ROC
    0.784
    best: 0.982 (RSRAE)
  • Unsupervised Anomaly Detection with Specified Settings -- 0.1% anomalyonFashion-MNIST
    AUC-ROC
    0.784
    best: 0.908 (Isolation Forest)
  • Unsupervised Anomaly Detection with Specified Settings -- 10% anomalyonFashion-MNIST
    AUC-ROC
    0.788
    best: 0.915 (IF)
  • Unsupervised Anomaly Detection with Specified Settings -- 10% anomalyonCIFAR-10
    AUC-ROC
    0.778
    best: 0.903 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 10% anomalyonMNIST
    AUC-ROC
    0.629
    best: 0.938 (LVAD)
  • Unsupervised Anomaly Detection with Specified Settings -- 10% anomalyonSTL-10
    AUC-ROC
    0.826
    best: 0.997 (Shell-Renormalized)
  • Unsupervised Anomaly Detection with Specified Settings -- 10% anomalyonCats and Dogs
    AUC-ROC
    0.96
    best: 0.996 (Shell-Renormalized)

Methodology6 results

  • Anomaly DetectiononVehicle Claims
    AUC· 2022-10-25
    51.22
    best: 98.65 (Random Forest)
    Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical EncodingsarXiv:2210.14056
  • Anomaly DetectiononASSIRA Cat Vs Dog
    AUC-ROC
    0.846
  • Anomaly DetectiononSTL-10
    AUC-ROC
    0.883
    best: 0.999 (Shell-Renormalized)
  • Anomaly DetectiononCIFAR-10
    AUC-ROC
    0.85
    best: 0.894 (Shell-Renormalized)
  • Anomaly DetectiononFashion-MNIST
    AUC-ROC
    0.769
    best: 0.889 (IF)
  • Anomaly DetectiononMNIST
    AUC-ROC
    0.613
    best: 0.904 (LVAD)

Graphs6 results

  • Unsupervised Anomaly DetectiononVehicle Claims
    AUC· 2022-10-25
    51.22
    best: 65.43 (SOM)
    Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical EncodingsarXiv:2210.14056
  • Unsupervised Anomaly DetectiononASSIRA Cat Vs Dog
    AUC-ROC
    0.846
  • Unsupervised Anomaly DetectiononSTL-10
    AUC-ROC
    0.883
    best: 0.999 (Shell-Renormalized)
  • Unsupervised Anomaly DetectiononCIFAR-10
    AUC-ROC
    0.85
    best: 0.894 (Shell-Renormalized)
  • Unsupervised Anomaly DetectiononFashion-MNIST
    AUC-ROC
    0.769
    best: 0.889 (IF)
  • Unsupervised Anomaly DetectiononMNIST
    AUC-ROC
    0.613
    best: 0.904 (LVAD)

Time Series2 results

  • Time Series AnalysisonUCR Anomaly Archive
    accuracy· 2023-11-21
    0.061
    best: 0.708 (TimeVQVAE-AD)
    Explainable Time Series Anomaly Detection using Masked Latent Generative ModelingarXiv:2311.12550
  • Time Series Anomaly DetectiononUCR Anomaly Archive
    accuracy· 2023-11-21
    0.061
    best: 0.708 (TimeVQVAE-AD)
    Explainable Time Series Anomaly Detection using Masked Latent Generative ModelingarXiv:2311.12550