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

TL;DR

DiffusionAD introduces a norm-guided, one-step denoising diffusion approach for anomaly detection that improves reconstruction quality and inference speed, outperforming current methods on multiple benchmarks.

Contribution

The paper proposes a novel diffusion-based anomaly detection pipeline with a rapid one-step denoising paradigm and a norm-guided reconstruction method, enhancing speed and accuracy.

Findings

Outperforms state-of-the-art methods on four benchmarks.

Achieves hundreds of times faster inference with comparable quality.

Effectively detects anomalies across diverse manifestations.

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