I'm trying to understand further how a variational autoencoder works beyond the conceptual level. However I'm still confused as to what the "vector of mean and variances" can look like and to digest it in a simplistic way.
For example, I understand that the latent variables in an autoencoder represents the compressed features of some input X and in the context of a variational autoencoder, you try to get the probabilistic distribution represented by mean and variance of the latent variable. So does this mean that:
a. If I have 5 latent variables in an autoencoder, in the context of a variational autoencoder, I should have 10 parameters (2 sets of mean and variances for each latent variables) represented as 2 vectors (1 vector of size 5 for means and 1 vector of size 5 for variances). In sampling/decoding, I can pass 5 means and 5 variances to generate an output.
b. Based on code examples, the representation of mean and variances are always 2 values (during sampling, you can randomize just a single mean and variance).
Thanks in advance. My goal is to be able to simplify the explanation since most examples online always give just 2 variables for decoding (just a single mean and variance value).