Position: Untrained Machine Learning for Anomaly Detection

Anomaly detection based on 3D point cloud data is an important research problem and receives more and more attention recently. Untrained anomaly detection based on only one sample is an emerging research problem motivated by real manufacturing industries such as personalized manufacturing that only one sample can be collected without any additional labels. How to accurately identify anomalies based on one 3D point cloud sample is a critical challenge in both industrial applications and the field of machine learning. This paper aims to provide a formal definition of untrained anomaly detection problem based on 3D point cloud data, discuss the differences between untrained anomaly detection and current unsupervised anomaly detection methods. Unlike unsupervised learning, untrained methods do not rely on any data, including unlabeled data. Instead, they leverage prior knowledge about the manufacturing surfaces and anomalies. Examples are used to illustrate these prior knowledge and untrained machine learning model. Afterwards, literature review on untrained anomaly detection based on 3D point cloud data is also provided, and the potential of untrained deep neural networks for anomaly detection is also discussed as outlooks.