Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits, they struggle to localize anomalies of varying scales, especially larger anomalies like entire missing components. Addressing this, we present a novel framework that enhances the capability of diffusion models, by extending the previous introduced implicit conditioning approach Meng et al. (2022) in three significant ways. First, we incorporate a dynamic step size computation that allows for variable noising steps in the forward process guided by an initial anomaly prediction. Second, we demonstrate that denoising an only scaled input, without any added noise, outperforms conventional denoising process. Third, we project images in a latent space to abstract away from fine details that interfere with reconstruction of large missing components. Additionally, we propose a fine-tuning mechanism that facilitates the model to effectively grasp the nuances of the target domain. Our method undergoes rigorous evaluation on two prominent anomaly detection datasets VISA and BTAD, yielding state-of-the-art performance. Importantly, our framework effectively localizes anomalies regardless of their scale, marking a pivotal advancement in diffusion-based anomaly detection.

翻译：扩散模型通过捕获正常数据分布并基于重构识别异常，已在异常检测领域展现出重要价值。尽管具有优势，此类模型难以定位尺度多变的异常，尤其是完整缺失组件这类大面积异常。针对这一问题，我们提出一种新型框架，通过三个关键改进增强扩散模型能力，对Meng等人(2022)先前提出的隐式条件化方法进行扩展。首先，我们引入动态步长计算方法，在前向过程中根据初始异常预测实现可变的加噪步数。其次，我们证明仅对缩放输入进行去噪（不添加任何噪声）优于传统去噪过程。第三，我们将图像投影至潜在空间，以消除干扰大面积缺失组件重构的细粒度细节。此外，我们提出一种微调机制，促使模型有效掌握目标域的细微特征。本方法在VISA和BTAD两个主流异常检测数据集上经过严格评估，取得最优性能。关键的是，本框架能有效定位任意尺度的异常，这标志着基于扩散模型的异常检测取得了重大突破。