Is it normal for the train loss to often be negative?

marco-rudolph / differnet

This is the official repository to the WACV 2021 paper "Same Same But DifferNet: Semi-Supervised Defect Detection with Normalizing Flows" by Marco Rudolph, Bastian Wandt and Bodo Rosenhahn.

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Is it normal for the train loss to often be negative? #45

Closed supersuperdong closed 9 months ago

supersuperdong commented 1 year ago

Furthermore, it seems that the train loss here is only seeking the parameters of the normalizing flow (NF) structure that can most likely transform the features y into a standard normal distribution z. How do we determine whether the feature extraction network is good or not?

marco-rudolph commented 12 months ago

Regarding the loss: https://github.com/marco-rudolph/differnet/issues/40 Regarding the features: You cannot really determine if the used features are helpful without having any anomalies - or creating synthetical ones. We use some kind of universal feature extractor by taking an ImageNet-pretrained network, which has shown to be effective in our experiments.