PyTorch-VAE
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Sampling Vanilla VAE
Hi, First, thanks for all the shared work ! I have a question concerning the sampling function in the Vanilla VAE. Why do you sample from a normal distribution (0,1) and not from a normal distribution with the learned parameters mu and sigma ? Since when we train the network we decode from the latent space over this distribution isnt more meaningful to sample from this distribution ? Maybe is there something I didnt get. Thank you again
I am not sure ideally it should be scaled like below i think.
mu/var comes out of the encoder network.
final_samples = mu + (sqrt(var) * samples)
I have the same question. @AntixK
When you do sampling, it means you did not have "input" images, you only sample from some distribution, here is a standard normal distribution. In other words, when sampling you did not have encoder, only use decoder, so you did not have this mu and var.