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This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data.
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Machine unlearning: A survey
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Jiawen Zhu, Choubo Ding, Yu Tian, and Guansong Pang · 2023
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A survey on visual anomaly detection: Challenge, approach, and prospect
Yunkang Cao, Xiaohao Xu, Jiangning Zhang, Yuqi Cheng, Xiaonan Huang, Guansong Pang, and Weiming Shen · 2024
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Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection
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