Related papers: Set Features for Fine-grained Anomaly Detection

Set Features for Fine-grained Anomaly Detection

URL: http://arxiv.org/abs/2302.12245v1
Date: Thu, 23 Feb 2023 18:58:57 GMT
Title: Set Features for Fine-grained Anomaly Detection
Authors: Niv Cohen. Issar Tzachor, Yedid Hoshen
Abstract summary: We propose 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.
Score: 32.36217153362305
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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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