Abstract
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.
Abstract (translated)
基于重构的方法在异常检测方面一直难以取得竞争性能。在本文中，我们介绍了去噪扩散异常检测(DDAD)，我们提出了一种基于目标图像的全新的去噪过程，以产生与目标图像非常相似的连贯恢复。随后，我们的异常检测框架利用目标图像作为输入图像的指导，以引导去噪过程，从而实现无缺陷恢复并保持名义模式。我们通过像素级和特征级比较输入和恢复图像来定位异常。最后，为了增强特征比较的有效性，我们引入了一种域适应方法，该方法利用我们Conditioned去噪过程生成的示例来微调特征提取器。该方法在包括MVTec和 VisA基准数据的各种数据集上进行了验证，分别实现了99.5%和99.3%的图像auROC水平。
URL
https://arxiv.org/abs/2305.15956
