Computer Science > Machine Learning
arXiv:2501.11430 (cs)
[Submitted on 20 Jan 2025 (v1), last revised 27 Feb 2025 (this version, v5)]
Title:A Survey on Diffusion Models for Anomaly Detection
Authors:Jing Liu,Zhenchao Ma,Zepu Wang,Chenxuanyin Zou,Jiayang Ren,Zehua Wang,Liang Song,Bo Hu,Yang Liu,Victor C.M. Leung
View a PDF of the paper titled A Survey on Diffusion Models for Anomaly Detection, by Jing Liu and 9 other authors
View PDFHTML (experimental)Abstract:Diffusion models (DMs) have emerged as a powerful class of generative AI models, showing remarkable potential in anomaly detection (AD) tasks across various domains, such as cybersecurity, fraud detection, healthcare, and manufacturing. The intersection of these two fields, termed diffusion models for anomaly detection (DMAD), offers promising solutions for identifying deviations in increasingly complex and high-dimensional data. In this survey, we review recent advances in DMAD research. We begin by presenting the fundamental concepts of AD and DMs, followed by a comprehensive analysis of classic DM architectures including DDPMs, DDIMs, and Score SDEs. We further categorize existing DMAD methods into reconstruction-based, density-based, and hybrid approaches, providing detailed examinations of their methodological innovations. We also explore the diverse tasks across different data modalities, encompassing image, time series, video, and multimodal data analysis. Furthermore, we discuss critical challenges and emerging research directions, including computational efficiency, model interpretability, robustness enhancement, edge-cloud collaboration, and integration with large language models. The collection of DMAD research papers and resources is available atthis https URL.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2501.11430 [cs.LG] |
| (orarXiv:2501.11430v5 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2501.11430 arXiv-issued DOI via DataCite |
Submission history
From: Jing Liu [view email][v1] Mon, 20 Jan 2025 12:06:54 UTC (108 KB)
[v2] Wed, 22 Jan 2025 05:32:29 UTC (354 KB)
[v3] Fri, 24 Jan 2025 09:58:41 UTC (354 KB)
[v4] Sun, 16 Feb 2025 22:35:44 UTC (353 KB)
[v5] Thu, 27 Feb 2025 02:05:55 UTC (353 KB)
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View a PDF of the paper titled A Survey on Diffusion Models for Anomaly Detection, by Jing Liu and 9 other authors
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