Insights of anomaly detection: How does polluted training data influence performance?

Anomaly detection is one of the most popular fields for computer vision in industrial applications. The idea of training machine learning only on defect-free objects saves enormous amounts of integration effort. The state of the art shows that current methods on public data sets (e.g. MVTec AD data set 1) have already solved the problem with AUROC segmentation scores of more than 99%. In real-world applications training data is not as "clean" as in public data sets. This work investigates the changes in detection performance when outliers end up in the training data. For this purpose, the training data is enriched step by step with images of defective objects. The AUROC score and the anomaly score is used as a quality criterion for performance measurement. We show that state of the art methods can be very robust, but that in some scenarios a draw down of 15 percentage points is possible.