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Arxiv
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Anomaly Detection and Generation with Diffusion Models: A Survey
- Anomaly detection (AD) is crucial in various domains like cybersecurity, finance, healthcare, and manufacturing by identifying unexpected patterns in real-world data.
- Diffusion models (DMs) in deep learning have gained interest for their ability to learn complex data distributions and generate high-fidelity samples, serving as a robust framework for unsupervised AD.
- A survey on anomaly detection and generation with diffusion models (ADGDM) analyzes theoretical foundations and practical implementations across different data types.
- Unlike previous surveys that treat AD and generation as separate, this survey emphasizes their synergistic relationship, showcasing how generation and detection methods can enhance each other.
- The survey categorizes ADGDM methods based on anomaly scoring mechanisms, conditioning strategies, and architectural designs, discussing their strengths and limitations.
- Key challenges like scalability and computational efficiency are highlighted along with future directions such as efficient architectures and integration with foundation models.
- The survey aims to assist researchers and practitioners in utilizing DMs for innovative AD solutions by synthesizing recent advances and identifying open research questions.
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