  Anomaly Detection (AD), as a critical problem, has been widely discussed. In
this paper, we specialize in one specific problem, Visual Defect Detection
(VDD), in many industrial applications. And in practice, defect image samples
are very rare and difficult to collect. Thus, we focus on the unsupervised
visual defect detection and localization tasks and propose a novel framework
based on the recent score-based generative models, which synthesize the real
image by iterative denoising through stochastic differential equations (SDEs).
Our work is inspired by the fact that with noise injected into the original
image, the defects may be changed into normal cases in the denoising process
(i.e., reconstruction). First, based on the assumption that the anomalous data
lie in the low probability density region of the normal data distribution, we
explain a common phenomenon that occurs when reconstruction-based approaches
are applied to VDD: normal pixels also change during the reconstruction
process. Second, due to the differences in normal pixels between the
reconstructed and original images, a time-dependent gradient value (i.e.,
score) of normal data distribution is utilized as a metric, rather than
reconstruction loss, to gauge the defects. Third, a novel $T$ scales approach
is developed to dramatically reduce the required number of iterations,
accelerating the inference process. These practices allow our model to
generalize VDD in an unsupervised manner while maintaining reasonably good
performance. We evaluate our method on several datasets to demonstrate its
effectiveness.
