🤖 AI Summary
Existing generative anomaly detection methods suffer from insufficient reconstruction quality, limiting detection accuracy and efficiency in industrial applications. To address this, we propose a novel “noise-to-norm” reconstruction paradigm: a one-step norm-guided diffusion model is designed with image norm as the explicit reconstruction objective, enabling high-fidelity anomaly-free reconstruction. We further introduce a multi-scale noise fusion mechanism and a reconstruction-preserving fast denoising strategy, accelerating inference by up to two orders of magnitude. Additionally, we devise a dual-branch architecture integrating reconstruction and segmentation, coupled with pixel-wise similarity analysis to generate precise anomaly score maps. Our method achieves state-of-the-art performance across four standard benchmarks, with substantially improved reconstruction fidelity and inference speed comparable to conventional (non-generative) approaches. The source code is publicly available.
📝 Abstract
Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, the anomalous region loses its distinctive features after being disturbed by Gaussian noise and is subsequently reconstructed into an anomaly-free one. Afterward, the segmentation sub-network predicts pixel-level anomaly scores based on the similarities and discrepancies between the input image and its anomaly-free reconstruction. Additionally, given the substantial decrease in inference speed due to the iterative denoising nature of diffusion models, we revisit the denoising process and introduce a rapid one-step denoising paradigm. This paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality. Furthermore, considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales, enhancing the fidelity of reconstructions. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches and achieves comparable inference speed, demonstrating the effectiveness and broad applicability of the proposed pipeline. Code is released at https://github.com/HuiZhang0812/DiffusionAD.
