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Models/LSTM-AE

LSTM-AE

Reported on 10 benchmarks across 3 tasks · 2 papers · 2 SOTA

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

Computer Vision6 results

Semi-supervised Anomaly DetectiononUBI-Fights
AUC· 2017-01-06
0.541
best: 0.846 (SS-Model + WS-Model + Sultani et al.)
SOTA
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Semi-supervised Anomaly DetectiononUBI-Fights
EER· 2017-01-06
0.48
best: 0.484 (Adversarial Generator)
SOTA
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Abnormal Event Detection In VideoonUBI-Fights
AUC· 2017-01-06
0.541
best: 0.906 (GMM)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Abnormal Event Detection In VideoonUBI-Fights
Decidability· 2017-01-06
0.059
best: 1.386 (GMM)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Abnormal Event Detection In VideoonUBI-Fights
EER· 2017-01-06
0.48
best: 0.484 (Adversarial Generator)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Semi-supervised Anomaly DetectiononUBI-Fights
Decidability· 2017-01-06
0.059
best: 1.108 (SS-Model + WS-Model + Sultani et al.)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546

Methodology4 results

Anomaly DetectiononUCR Anomaly Archive
Average F1· 2022-12-27
0.314
best: 0.47 (Auto-Encoder with Regression (AER))
AER: Auto-Encoder with Regression for Time Series Anomaly Detection arXiv:2212.13558
Anomaly DetectiononUBI-Fights
AUC· 2017-01-06
0.541
best: 0.906 (GMM)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Anomaly DetectiononUBI-Fights
Decidability· 2017-01-06
0.059
best: 1.386 (GMM)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Anomaly DetectiononUBI-Fights
EER· 2017-01-06
0.48
best: 0.484 (Adversarial Generator)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546