Abstract
The recent rapid development of deep learning has laid a milestone in visual anomaly detection (VAD). In this paper, we provide a comprehensive review of deep learning-based visual 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 VAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for visual 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 this https URL
Abstract (translated)
深度学习的最近快速发展已经在视觉异常检测(VAD)领域建立了一个里程碑。在本文中,我们全面审查了基于深度学习的视觉异常检测技术,从神经网络架构、监督级别、损失函数、指标和数据集等方面出发。此外,我们从工业制造中提取了新的设置,并对我们的新设置提出了新的VAD方法进行审查。此外,我们重点讨论了视觉异常检测中几个潜在的挑战。不同监督下代表性网络架构的优点和缺点 are discussed。最后,我们总结研究结论并指出未来的研究方向。更多资源可访问这个 https URL。
URL
https://arxiv.org/abs/2301.11514
