We aim at constructing a high performance model for defect detection that
detects unknown anomalous patterns of an image without anomalous data. To this
end, we propose a two-stage framework for building anomaly detectors using
normal training data only. We first learn self-supervised deep representations
and then build a generative one-class classifier on learned representations. We
learn representations by classifying normal data from the CutPaste, a simple
data augmentation strategy that cuts an image patch and pastes at a random
location of a large image. Our empirical study on MVTec anomaly detection
dataset demonstrates the proposed algorithm is general to be able to detect
various types of real-world defects. We bring the improvement upon previous
arts by 3.1 AUCs when learning representations from scratch. By transfer
learning on pretrained representations on ImageNet, we achieve a new
state-of-theart 96.6 AUC. Lastly, we extend the framework to learn and extract
representations from patches to allow localizing defective areas without
annotations during training.

该研究旨在构建一个高性能的缺陷检测模型，能够在未有异常数据的情况下检测图像中未知的异常模式。作者提出了一个两阶段框架，使用仅有的正常数据来进行异常检测，第一阶段学习自监督的深度表示，第二阶段建立基于学习表示的生成单类分类器。该方法在 MVTec 异常检测数据集上表现良好，并在学习表示时相较于现有技术提升了 3.1 AUC。作者还通过迁移学习，在 ImageNet 预训练模型基础上实现了 96.6 AUC 的最优表现，并扩展了该框架以从补丁学习和提取表示，无需训练期间标注即可定位损坏区域。