Unsupervised Visual Defect Detection with Score-Based Generative Model

• URL: http://arxiv.org/abs/2211.16092v1
• Date: Tue, 29 Nov 2022 11:06:29 GMT
• Title: Unsupervised Visual Defect Detection with Score-Based Generative Model
• Authors: Yapeng Teng, Haoyang Li, Fuzhen Cai, Ming Shao, Siyu Xia
• Abstract summary: We focus on the unsupervised visual defect detection and localization tasks. We propose a novel framework based on the recent score-based generative models. We evaluate our method on several datasets to demonstrate its effectiveness.
• Score: 17.610722842950555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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