Embeddings are getting clustered together in a small region after training

[tamerthamoqa]

Embeddings are getting clustered together in a small region after training

[tamerthamoqa]

Hello @Nrohlable

I am assuming you used Triplet loss, which optimizes the embedding space for Euclidean Distance and not Cosine Distance. Does using Euclidean Distance instead of Cosine Distance also have similar results?

[Nrohlable]

@tamerthamoqa thanks for responding.

Yes even if we use Euclidean distance the story doesn't changes much in that case as well
Earlier for one pair we were getting around 1.29 and afterwards for the same pair it was 0.07288.

Also earlier, with the pre-trained models the overlap of euclidean distance btw same person image and different person image was around 8% and after after this training it went to around 81%.

Is there any specific norm which I used maintain while training this kind of network, Like this network has to be trained for lets say 200 epochs in order get some valid result or something.