Toward generalist anomaly detection via in-context residual learning with few-shot sample prompts

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. Some recent studies have showed that large...

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Bibliographic Details
Main Authors: ZHU, Jiawen, PANG, Guansong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9762
https://ink.library.smu.edu.sg/context/sis_research/article/10762/viewcontent/2403.06495v3.pdf
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Institution: Singapore Management University
Language: English

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