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Models/CAE-M

CAE-M

Reported on 8 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Methodology4 results

Anomaly DetectiononSMAP
AUC· 2021-07-27
99.01
best: 99.21 (TranAd)
SOTA
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626
Anomaly DetectiononSMAP
F1· 2021-07-27
88.27
best: 94.1 (DFM (flow matching))
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626
Anomaly DetectiononSMAP
Precision· 2021-07-27
81.93
best: 89.7 (DFM (flow matching))
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626
Anomaly DetectiononSMAP
Recall· 2021-07-27
95.67
best: 99.99 (TranAd)
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626

Graphs4 results

Unsupervised Anomaly DetectiononSMAP
AUC· 2021-07-27
99.01
best: 99.21 (TranAd)
SOTA
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626
Unsupervised Anomaly DetectiononSMAP
F1· 2021-07-27
88.27
best: 94.1 (DFM (flow matching))
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626
Unsupervised Anomaly DetectiononSMAP
Precision· 2021-07-27
81.93
best: 89.7 (DFM (flow matching))
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626
Unsupervised Anomaly DetectiononSMAP
Recall· 2021-07-27
95.67
best: 99.99 (TranAd)
Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals arXiv:2107.12626