Set Features for Fine-grained Anomaly Detection

23 Feb 2023  ·  Niv Cohen, Issar Tzachor, Yedid Hoshen ·

Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as normal or anomalous and then classify the entire sample as anomalous if it contains anomalous elements. However, such approaches do not extend to scenarios where the anomalies are expressed by an unusual combination of normal elements. In this paper, we overcome this limitation by proposing set features that model each sample by the distribution its elements. We compute the anomaly score of each sample using a simple density estimation method. Our simple-to-implement approach outperforms the state-of-the-art in image-level logical anomaly detection (+3.4%) and sequence-level time-series anomaly detection (+2.4%).

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Anomaly Detection MVTec LOCO AD SINBAD Avg. Detection AUROC 86.8 # 4
Detection AUROC (only logical) 88.9 # 2
Detection AUROC (only structural) 84.7 # 13
Anomaly Detection UEA time-series datasets SINBAD Avg. ROC-AUC 96.8 # 1


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