Reconstruction-based approaches have achieved remarkable outcomes in anomaly
detection. The exceptional image reconstruction capabilities of recently
popular diffusion models have sparked research efforts to utilize them for
enhanced reconstruction of anomalous images. Nonetheless, these methods might
face challenges related to the preservation of image categories and pixel-wise
structural integrity in the more practical multi-class setting. To solve the
above problems, we propose a Difusion-based Anomaly Detection (DiAD) framework
for multi-class anomaly detection, which consists of a pixel-space autoencoder,
a latent-space Semantic-Guided (SG) network with a connection to the stable
diffusion's denoising network, and a feature-space pre-trained feature
extractor. Firstly, The SG network is proposed for reconstructing anomalous
regions while preserving the original image's semantic information. Secondly,
we introduce Spatial-aware Feature Fusion (SFF) block to maximize
reconstruction accuracy when dealing with extensively reconstructed areas.
Thirdly, the input and reconstructed images are processed by a pre-trained
feature extractor to generate anomaly maps based on features extracted at
different scales. Experiments on MVTec-AD and VisA datasets demonstrate the
effectiveness of our approach which surpasses the state-of-the-art methods,
e.g., achieving 96.8/52.6 and 97.2/99.0 (AUROC/AP) for localization and
detection respectively on multi-class MVTec-AD dataset. Code will be available
at this https URL

基于扩散模型的异常检测中，提出了一种适用于多类异常检测的扩散异常检测（DiAD）框架，包括像素空间自动编码器、与稳定扩散去噪网络相连的潜空间语义引导（SG）网络，以及特征空间预训练特征提取器。通过实验证明了该方法的有效性，并超过了多类 MVTec-AD 数据集上的最先进方法，即对于多类 MVTec-AD 数据集的本地化和检测，达到了 96.8/52.6 和 97.2/99.0（AUROC/AP）。