Pretty sure all this says is to minimize KL-Divergence instead of log-likelihood (for the encoder), or your latent variables are garbage. Judging by many examp

Pretty sure all this says is to minimize KL-Divergence instead of log-likelihood (for the encoder), or your latent variables are garbage. Judging by many examples I've seen of VAEs in ML, this is not news. This post [0] does a good job showing the difference between latent variables for log-likelihood and KL losses. [0]https://towardsdatascience.com/intuitively-understanding-variational-autoencoders-1bfe67eb5daf https://towardsdatascience.com/intuitively-understanding-var...