Unsupervised Anomaly Detection On Vehicle
Metrics
AUC
Results
Performance results of various models on this benchmark
Model Name | AUC | Paper Title | Repository |
|---|---|---|---|
| NeuTraL-AD | 57.03 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| SOM | 65.43 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| RSRAE | 55.38 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| Latent Outlier Exposure | 58.59 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| Isolation Forest | 59.42 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| One Class Support Vector Machines | 51.68 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| Local Outlier Factor | 52.86 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| DAGMM | 51.22 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings | |
| SOM-DAGMM | 53.82 | Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings |
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