Defect detection aims to detect and localize regions out of the normal
distribution. Previous works rely on modeling the normality to identify the
defective regions, which may lead to non-ideal generalizability. This paper
proposed 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.

该论文提出一种无需建模过程，通过生成对抗网络（GAN）、新提出的缩放模式损失和动态遮蔽循环次要网络等三方面联合作用直接检测缺陷模式的一阶段框架，在 MVTec AD 数据集的质地类别中表现出比现有技术更高的 F1 分数和更好的普适性。