Defect detection aims to detect and localize regions out of the normal distribution.Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizability.This paper proposes a one-stage framework that detects defective patterns directly without the modeling process.This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping.Experimental results on the texture class of the challenging MVTec AD dataset show that the proposed method is 2.9\% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability.

翻译：缺陷检测旨在检测并定位偏离正常分布的区域。现有方法通常通过建模正常性并与输入进行比较来识别缺陷区域，这可能会限制其泛化能力。本文提出了一种无需建模过程即可直接检测缺陷模式的单阶段框架。该能力通过三方协同实现：生成对抗网络（GAN）、新提出的尺度化模式损失（scaled pattern loss）以及动态掩膜循环一致性辅助网络。我们有意排除了可能指示缺陷位置的显式信息，以避免学习任何直接映射关系。在具有挑战性的MVTec AD数据集纹理类上的实验结果表明，所提方法在F1分数上比现有最优方法（SOTA）高出2.9%，同时在泛化性能上显著优于SOTA方法。