Unsupervised Visual Defect Detection with Score-Based Generative Model

Understand

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).