The question is actually less broad than it sounds. I generally do understand how variational autoencoders work.
From the encoding step we get four parts:
- mean $\mu$
- standard deviation $\sigma$
- random value from normal distribution $\epsilon$
- sample $z$
For the training process of the autoencoder, the decoder is fed with the sample $z$. If we wish to generate new samples, we manipulate the code that is fed to the decoder.
What output from the encoder to use for a pure encoding purpose (given a fully trained VAE)?
Please provide explanations and if possible a mathematical intuition / reasoning. If you have additional sources where this aspect is explained, please add links.
I'm deliberately and specifically using VAEs, that is not the question here.