Unsupervised anomaly detection in MeV ultrafast electron diffraction
This work addresses the problem of automating fault detection in MUED data for researchers, but it appears incremental as it applies existing unsupervised techniques to a specific domain.
The study developed an unsupervised anomaly detection method to identify faulty images in MeV ultrafast electron diffraction (MUED) without manual labeling, providing users with uncertainty measures for decision-making.
This study focus in the construction of an unsupervised anomaly detection methodology to detect faulty images in MUED. We believe that unsupervised techniques are the best choice for our purposes because the data used to train the detector does not need to be manually labeled, and instead, the machine is intended to detect by itself the anomalies in the dataset, which liberates the user of tedious, time-consuming initial image examination. The structure must, additionally, provide the user with some measure of uncertainty in the detection, so the user can take decisions based on this measure.