I'm training an AutoEncoder-like network to take a face embedding, encode it, decode it, and then I calculate the loss between the input and output embedding. The input embedding is calculated by passing an image through a pre-trained frozen network. Is there a difference between pre-computing the embeddings from the frozen network before training and computing them during training to have gradients?



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