用扩散模型做异常检测，效果优于传统方法。
Anomaly detection using Diffusion-based methods
- 基于重建误差的扩散模型检测异常
- 在高维数据上表现更稳定准确
- 适合复杂真实场景的异常识别
本文研究了基于扩散模型的异常检测方法在紧凑型与高分辨率数据集上的有效性。评估了去噪扩散概率模型（DDPM）和扩散Transformer（DiT）在重建目标下的性能，并与孤立森林、一类SVM及COPOD等传统方法对比。结果表明，扩散模型在处理复杂真实异常检测任务时具备更强的适应性、可扩展性和鲁棒性。关键发现指出重建误差对提升检测精度至关重要，并证实了模型在高维数据上的良好可扩展性。未来工作包括优化编码器-解码器结构，探索多模态数据以进一步推进该领域。
原文摘要 · Abstract (English)
This paper explores the utility of diffusion-based models for anomaly detection, focusing on their efficacy in identifying deviations in both compact and high-resolution datasets. Diffusion-based architectures, including Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs), are evaluated for their performance using reconstruction objectives. By leveraging the strengths of these models, this study benchmarks their performance against traditional anomaly detection methods such as Isolation Forests, One-Class SVMs, and COPOD. The results demonstrate the superior adaptability, scalability, and robustness of diffusion-based methods in handling complex real-world anomaly detection tasks. Key findings highlight the role of reconstruction error in enhancing detection accuracy and underscore the scalability of these models to high-dimensional datasets. Future directions include optimizing encoder-decoder architectures and exploring multi-modal datasets to further advance diffusion-based anomaly detection.
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