DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

• URL: http://arxiv.org/abs/2303.08730v3
• Date: Fri, 24 Nov 2023 11:20:20 GMT
• Title: DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection
• Authors: Hui Zhang, Zheng Wang, Zuxuan Wu, Yu-Gang Jiang
• Abstract summary: We reformulate the reconstruction process using a diffusion model into a noise-to-norm paradigm. We propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration.
• Score: 89.49600182243306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, anomalous regions are perturbed with Gaussian noise and reconstructed as normal, overcoming the limitations of previous models by facilitating anomaly-free restoration. Additionally, we propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. Furthermore, the introduction of the norm-guided paradigm elevates the accuracy and fidelity of reconstructions. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches, demonstrating the effectiveness and broad applicability of the proposed pipeline.

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