Anomaly Detection On Mpdd
评估指标
Detection AUROC
Segmentation AUROC
评测结果
各个模型在此基准测试上的表现结果
比较表格
| 模型名称 | Detection AUROC | Segmentation AUROC |
|---|---|---|
| glad-towards-better-reconstruction-with | 97.5 | 98.7 |
| towards-total-online-unsupervised-anomaly | 87.4 | 97.8 |
| adaclip-adapting-clip-with-hybrid-learnable | 82.5 | 96.1 |
| anomaly-detection-using-score-based | - | 96.8 |
| towards-total-recall-in-industrial-anomaly | 82.12 | 95.66 |
| revisiting-deep-feature-reconstruction-for | 95.73 | 97.45 |
| dual-modeling-decouple-distillation-for | 98.10 | 98.96 |
| fastrecon-few-shot-industrial-anomaly | 82.5 | 97.9 |
| diffusionad-denoising-diffusion-for-anomaly | 96.2 | 98.5 |
| produce-once-utilize-twice-for-anomaly | 97.5 | - |
| a-unified-anomaly-synthesis-strategy-with | 99.6 | 99.4 |
| dinomaly-the-less-is-more-philosophy-in-multi | 97.2 | 99.1 |
| kanoclip-zero-shot-anomaly-detection-through | 77.8 | 98.3 |
| realnet-a-feature-selection-network-with | 96.3 | 98.2 |
| progressive-boundary-guided-anomaly-synthesis | 97.7 | 98.8 |