Based on the Torch implementation of a vanilla variational auto-encoder in a previous article, this article discusses an implementation of a denoising variational auto-encoder. While the theory of denoising variational auto-encoders is more involved, an implementation merely requires a suitable noise model.
After formally introducing the concept of categorical variational auto-encoders in a previous article, this article presents a practical Torch implementation of variational auto-encoders with Bernoulli latent variables.