  Diffusion models have shown superior performance on unsupervised anomaly
detection tasks. Since trained with normal data only, diffusion models tend to
reconstruct normal counterparts of test images with certain noises added.
However, these methods treat all potential anomalies equally, which may cause
two main problems. From the global perspective, the difficulty of
reconstructing images with different anomalies is uneven. Therefore, instead of
utilizing the same setting for all samples, we propose to predict a particular
denoising step for each sample by evaluating the difference between image
contents and the priors extracted from diffusion models. From the local
perspective, reconstructing abnormal regions differs from normal areas even in
the same image. Theoretically, the diffusion model predicts a noise for each
step, typically following a standard Gaussian distribution. However, due to the
difference between the anomaly and its potential normal counterpart, the
predicted noise in abnormal regions will inevitably deviate from the standard
Gaussian distribution. To this end, we propose introducing synthetic abnormal
samples in training to encourage the diffusion models to break through the
limitation of standard Gaussian distribution, and a spatial-adaptive feature
fusion scheme is utilized during inference. With the above modifications, we
propose a global and local adaptive diffusion model (abbreviated to GLAD) for
unsupervised anomaly detection, which introduces appealing flexibility and
achieves anomaly-free reconstruction while retaining as much normal information
as possible. Extensive experiments are conducted on three commonly used anomaly
detection datasets (MVTec-AD, MPDD, and VisA) and a printed circuit board
dataset (PCB-Bank) we integrated, showing the effectiveness of the proposed
method.
