I recently worked on Variational(VAE) Autoencoder. For me, Autoencoder works like this:
In the loss function, we have
- a reconstruction error term which allows us to minimize the distance between input and output.
- a regularization term which allows us to match the latent variables(z1,z2) to a unit gaussian distribution.
But does someone know why do we introduce the random effect? To match a gaussian distribution, why not just use a gaussian likehood as a regularization term of loss function?
The answer might be from a bayes prospective of VAE autoencoder? Even a small hint might be of great help, thanks!