The problem about the evaluation about rd4ad HOT 1 OPEN

Hello,

In my opinion, the "Anomaly" defined in anomaly detection should be unknown, so we shouldn't validate the algorithm on real anomaly dataset. However, we know that it's necessary to find a validation procedure to tune the hyper-parameters. Recently, I find some studies related to this:
(1) We can generate pseudo anomaly dataset to tune the model. http://medicalood.dkfz.de/web/
(2) As the number of iterations increases the model should converge to the optimum, so we may not need to use the validation set to find the best epoch. https://github.com/zhiyuanyou/UniAD

I think the study area of anomaly detection is still developing and recent studies are aiming to solve this problem.

from rd4ad.