Tabular Anomaly Detection on Wine
1AUC-ROCCausalTAD-135M
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CausalTAD-135Mbackbone=SmolLM-135M2026.02 | 1 | — | |
| DRL2026.02 | 1 | — | |
| CTADBase Detector=KNN2026.02 | 1 | — | |
| CTADBase Detector=DRL2026.02 | 1 | — | |
| DRL2025.10 | 1 | — | |
| LLM-DASMethod Variant=DRL2025.10 | 1 | — | |
| MCM2026.05 | 0.9988 | — | |
| GOAD2026.05 | 0.9986 | — | |
| RTTAD2026.05 | 0.9986 | — | |
| DTE2026.02 | 0.9944 | — | |
| DTE2025.10 | 0.9944 | — | |
| SLAD2026.05 | 0.9944 | — | |
| NeuTraLAD2026.05 | 0.9931 | — | |
| DRL2026.05 | 0.9931 | — | |
| KNN2026.02 | 0.9917 | — | |
| KNN2025.10 | 0.9917 | — | |
| ICL2026.05 | 0.9889 | — | |
| TTAD2026.05 | 0.9828 | — | |
| NeuTraLAD2026.02 | 0.9753 | — | |
| ICAD-LLMMethod Category=Task-Specific2025.12 | 0.9635 | — | |
| NPT-AD2025.10 | 0.9622 | — | |
| CTADBase Detector=MCM2026.02 | 0.9586 | — | |
| DIF2026.05 | 0.9542 | — | |
| MCM2026.02 | 0.9538 | — | |
| MCM2025.10 | 0.9538 | — | |
| MCMMethod Category=Task-Specific2025.12 | 0.9388 | — | |
| ICL2026.02 | 0.915 | — | |
| ICL2025.10 | 0.915 | — | |
| AnoLLMModel Size=135M2025.10 | 0.909 | — | |
| SLAD2026.02 | 0.902 | — | |
| ICAD-LLMMethod Category=Universal2025.12 | 0.8945 | — | |
| ACRMethod Category=Universal2025.12 | 0.8884 | — | |
| UniADMethod Category=Universal2025.12 | 0.8793 | — | |
| DAGMMMethod Category=Task-Specific2025.12 | 0.8648 | — | |
| LUNAR2026.05 | 0.8611 | — | |
| AnoLLMModel Size=360M2025.10 | 0.851 | — | |
| GOADMethod Category=Task-Specific2025.12 | 0.8343 | — | |
| REPEN2026.02 | 0.808 | — | |
| NeuTraL ADMethod Category=Universal2025.12 | 0.8054 | — | |
| Iforest2026.05 | 0.7736 | — | |
| AnoLLM-135Mbackbone=SmolLM-135M2026.02 | 0.758 | — | |
| RCA2026.02 | 0.747 | — | |
| ECOD2026.02 | 0.7433 | — | |
| ECOD2025.10 | 0.7433 | — | |
| ECOD2026.02 | 0.743 | — | |
| KNN2026.02 | 0.707 | — | |
| DeepSVDD2026.02 | 0.683 | — | |
| IForest2026.02 | 0.6571 | — | |
| IForest2025.10 | 0.6571 | — | |
| IForestMethod Category=Task-Specific2025.12 | 0.6379 | — | |
| LOF2026.05 | 0.625 | — | |
| EPHAD2026.05 | 0.6241 | — | |
| ECOD2026.05 | 0.5875 | — | |
| GOAD2026.02 | 0.5366 | — | |
| AutoEncoder2026.02 | 0.5356 | — | |
| AutoEncoder2025.10 | 0.5356 | — | |
| DeepSVDD2026.05 | 0.5278 | — | |
| DTE2026.02 | 0.508 | — | |
| DeepSVDD2026.02 | 0.5067 | — | |
| DeepSVDD2025.10 | 0.5067 | — | |
| OCSVM2026.05 | 0.5 | — | |
| OCSVM2026.02 | 0.485 | — | |
| OCSVM2025.10 | 0.485 | — | |
| LLM-DASMethod Variant=PCA2025.10 | 0.4557 | — | |
| PCA2026.02 | 0.4467 | — | |
| PCA2025.10 | 0.4467 | — | |
| PCA2026.02 | 0.438 | — | |
| LOF2025.10 | 0.4083 | — | |
| IForest2026.02 | 0.385 | — | |
| ICL2026.02 | 0.277 | — | |
| NeuTral2026.02 | 0.218 | — | |
| GOAD2026.02 | 0.125 | — | |
| DeepSVDDlibrary=PyOD2026.05 | — | 0.2273 | |
| DIFlibrary=DeepOD2026.05 | — | 0.6 | |
| DRL2026.05 | — | 0.9 | |
| ECODlibrary=PyOD2026.05 | — | 0.2727 | |
| EPHAD2026.05 | — | 0.3 | |
| GOADlibrary=DeepOD2026.05 | — | 0.9 | |
| ICLlibrary=DeepOD2026.05 | — | 0.9 | |
| Iforestlibrary=PyOD2026.05 | — | 0.5 | |
| LOFlibrary=PyOD2026.05 | — | 0.2703 | |
| LUNARlibrary=PyOD2026.05 | — | 0.5 | |
| MCM2026.05 | — | 0.9 | |
| NeuTraLADlibrary=DeepOD2026.05 | — | 0.9 | |
| OCSVMlibrary=PyOD2026.05 | — | 0.2174 | |
| RTTADparameter k=32026.05 | — | 0.9 | |
| SLADlibrary=DeepOD2026.05 | — | 0.9 | |
| TTAD2026.05 | — | 0.8 |