Reconstruction-based methods have struggled to achieve competitive performance on anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (DDAD). We propose a novel denoising process for image reconstruction conditioned on a target image. This results in a coherent restoration that closely resembles the target image. Subsequently, our anomaly detection framework leverages this conditioning where the target image is set as the input image to guide the denoising process, leading to defectless reconstruction while maintaining nominal patterns. We localise anomalies via a pixel-wise and feature-wise comparison of the input and reconstructed image. Finally, to enhance the effectiveness of feature comparison, we introduce a domain adaptation method that utilises generated examples from our conditioned denoising process to fine-tune the feature extractor. The veracity of the approach is demonstrated on various datasets including MVTec and VisA benchmarks, achieving state-of-the-art results of 99.5% and 99.3% image-level AUROC respectively.

翻译：基于重构的方法在异常检测任务中一直难以取得具有竞争力的性能。本文提出去噪扩散异常检测（DDAD），我们设计了一种以目标图像为条件的图像重构去噪新流程，该流程能够实现与目标图像高度相似的一致性修复。随后，我们的异常检测框架利用这种条件机制，将输入图像作为目标图像来引导去噪过程，从而在保持正常模式的同时实现无缺陷重建。通过像素级和特征级的输入图像与重构图像对比，我们能够定位异常区域。最后，为增强特征对比的有效性，我们引入一种领域自适应方法，利用条件去噪过程中生成的样本微调特征提取器。该方法的有效性在包括 MVTec 和 VisA 基准测试在内的多个数据集上得到验证，分别取得了 99.5% 和 99.3% 的图像级 AUROC 最新成果。