March 14, 2024, 4:43 a.m. | Yizhou Wang, Dongliang Guo, Sheng Li, Octavia Camps, Yun Fu

cs.LG updates on arXiv.org arxiv.org

arXiv:2302.06670v2 Announce Type: replace
Abstract: Anomaly detection and localization of visual data, including images and videos, are of great significance in both machine learning academia and applied real-world scenarios. Despite the rapid development of visual anomaly detection techniques in recent years, the interpretations of these black-box models and reasonable explanations of why anomalies can be distinguished out are scarce. This paper provides the first survey concentrated on explainable visual anomaly detection methods. We first introduce the basic background of image-level …

anomaly anomaly detection arxiv cs.ai cs.lg detection images survey type videos

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