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Models/OC-SVM

OC-SVM

Reported on 10 benchmarks across 4 tasks · 2 papers

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

Methodology4 results

Anomaly DetectiononAnoShift
ROC-AUC FAR· 2022-06-30
49.57
best: 62.5 (ACR-NTL (zero-shot, test anomaly ratio=1%))
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476
Anomaly DetectiononAnoShift
ROC-AUC IID· 2022-06-30
76.86
best: 92.67 (deepSVDD)
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476
Anomaly DetectiononAnoShift
ROC-AUC NEAR· 2022-06-30
71.43
best: 87 (deepSVDD)
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476
Anomaly DetectiononAnoShift
ROC-AUC-ID (In-Distribution setup)· 2022-06-30
68.73
best: 88.24 (deepSVDD)
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476

Graphs4 results

Unsupervised Anomaly DetectiononAnoShift
ROC-AUC FAR· 2022-06-30
49.57
best: 62.5 (ACR-NTL (zero-shot, test anomaly ratio=1%))
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476
Unsupervised Anomaly DetectiononAnoShift
ROC-AUC IID· 2022-06-30
76.86
best: 92.67 (deepSVDD)
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476
Unsupervised Anomaly DetectiononAnoShift
ROC-AUC NEAR· 2022-06-30
71.43
best: 87 (deepSVDD)
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476
Unsupervised Anomaly DetectiononAnoShift
ROC-AUC-ID (In-Distribution setup)· 2022-06-30
68.73
best: 88.24 (deepSVDD)
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection arXiv:2206.15476

Time Series2 results

Time Series AnalysisonUCR Anomaly Archive
accuracy· 2023-11-21
0.088
best: 0.708 (TimeVQVAE-AD)
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling arXiv:2311.12550
Time Series Anomaly DetectiononUCR Anomaly Archive
accuracy· 2023-11-21
0.088
best: 0.708 (TimeVQVAE-AD)
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling arXiv:2311.12550