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.

扩展前述的隐式条件方法，我们提出了一种新的框架，通过动态步长计算、无噪声缩放输入和潜在空间投影的方式增强了扩散模型的能力，有效地定位异常并在两个著名异常检测数据集上取得了最先进的性能。