If I have an image dataset that consists of "normal", anomalous and ground truth image data, how do I make use of the ground truth data?
To my understanding if I train an unsupervised anomaly detection approach on "normal" images, it can then later predict whether an input image is "normal" or anomalous.
How can I make use of the ground truth data in this context? And what purpose has the ground truth data in anomaly detection in general?
Thanks in advance for any answers! I am relatively new to the field of anomaly detection.