I understand the basic structure of variational autoencoder and normal (deterministic) autoencoder and the math behind them, but when and why would I prefer one type of autoencoder to the other? All I can think about is the prior distribution of latent variables of variational autoencoder allows us to sample the latent variables and then construct the new image. What advantage does the stochasticity of variational autoencoder over the deterministic autoencoder?