Anomaly detection task

Anomaly detection task

Anomaly detection is a semi-supervised task of finding patterns that considerably deviate from standard patterns. When you apply the method of anomaly detection, you would first feed the model with a dataset of normal patterns. After the initial training on the dataset with regular patterns, the second round of training would include a dataset with anomalous patterns. After this operation, the model can differentiate “anomalous” from “normal” since the normal patterns appear more often than abnormal patterns. In this task, you may be required to label images with only regular patterns.

The Intel® Geti™ platform supports 1 anomaly task type:

• Anomaly detection

When you create an anomaly detection project, a classifier labels the whole image either as “normal” or “anomalous”.