  Recent unsupervised anomaly detection methods often rely on feature
extractors pretrained with auxiliary datasets or on well-crafted
anomaly-simulated samples. However, this might limit their adaptability to an
increasing set of anomaly detection tasks due to the priors in the selection of
auxiliary datasets or the strategy of anomaly simulation. To tackle this
challenge, we first introduce a prior-less anomaly generation paradigm and
subsequently develop an innovative unsupervised anomaly detection framework
named GRAD, grounded in this paradigm. GRAD comprises three essential
components: (1) a diffusion model (PatchDiff) to generate contrastive patterns
by preserving the local structures while disregarding the global structures
present in normal images, (2) a self-supervised reweighting mechanism to handle
the challenge of long-tailed and unlabeled contrastive patterns generated by
PatchDiff, and (3) a lightweight patch-level detector to efficiently
distinguish the normal patterns and reweighted contrastive patterns. The
generation results of PatchDiff effectively expose various types of anomaly
patterns, e.g. structural and logical anomaly patterns. In addition, extensive
experiments on both MVTec AD and MVTec LOCO datasets also support the
aforementioned observation and demonstrate that GRAD achieves competitive
anomaly detection accuracy and superior inference speed.
