Questions about L2 Normalization

[riverHu233]

Hi @tamerthamoqa ,
I'm curious about L2 Normalization, which would constrain the embedding into an euclidean feature space and , so the maximum distance of two features in feature space shouldn't be 2? why the threshold is from 0.0 to 4.0?
Thanks!

[riverHu233]

Hi @tamerthamoqa
I thought I got it, the reason why the threshold is from 0.0 to 4.0 is that 2 different features belong to different feature space, and the minimum distances of these two features is 4.0, bigger than that , the features can seperately for sure. Thanks for the project!

[tamerthamoqa]

Hello riverHu233,

My interpretation might be incorrect, but according to the facenet paper (description of figure 1) it implies that the squared l2-norm space might be [0, 4] though please keep in mind I am not sure about this since I haven't looked too deeply into this matter. There are discussion threads about this topic in the David Sandberg 'facenet' github repository but I haven't found a clear answer, at least, from what I remember.

From what I have seen, all pytorch implementations of facenet on github use the same threshold range so that is the main reason why I went with that range, but to be honest, I need to look into it to get a clearer understanding myself.