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Models/LSTM-VAE

LSTM-VAE

Reported on 10 benchmarks across 4 tasks · 4 papers · 5 SOTA

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

Methodology5 results

Anomaly Detectiononvoraus-AD
Avg. Detection AUROC· 2017-11-02
86.7
best: 93.6 (MVT-Flow)
SOTA
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder arXiv:1711.00614
Anomaly DetectiononUBI-Fights
AUC· 2017-01-06
0.541
best: 0.906 (GMM)
SOTA
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)
SOTA
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546
Anomaly DetectiononUCR Anomaly Archive
Average F1· 2022-12-27
0.317
best: 0.47 (Auto-Encoder with Regression (AER))
AER: Auto-Encoder with Regression for Time Series Anomaly Detection arXiv:2212.13558
Anomaly DetectiononUBI-Fights
Decidability· 2017-01-06
0.059
best: 1.386 (GMM)
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder arXiv:1701.01546

Computer Vision3 results

Abnormal Event Detection In VideoonUBI-Fights
AUC· 2017-01-06
0.541
best: 0.906 (GMM)
SOTA
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)
SOTA
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

Time Series2 results

Time Series AnalysisonUCR Anomaly Archive
accuracy· 2023-11-21
0.198
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
0.198
best: 0.708 (TimeVQVAE-AD)
Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling arXiv:2311.12550