深度工业图像异常检测：综述
Deep Industrial Image Anomaly Detection: A Survey
AI总结：
本文系统综述了基于深度学习的工业图像异常检测方法，涵盖架构、监督级别、损失函数、指标与数据集，并提出新设定和未来研究方向。
摘要：
深度学习近年来的快速发展为工业图像异常检测（IAD）树立了一个里程碑。本文从神经网络架构、监督级别、损失函数、评价指标和数据集等视角，对基于深度学习的图像异常检测技术进行了全面综述。此外，我们从工业制造中提炼出新的设定，并在所提出的新设定下回顾了当前的IAD方法。同时，我们强调了图像异常检测面临的若干开放性挑战。文中还讨论了代表性网络架构在不同监督级别下的优点与不足。最后，我们总结了研究发现并指出了未来的研究方向。更多资源可在 https://github.com/M-3LAB/awesome-industrial-anomaly-detection 获取。
英文摘要：
The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
