Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. (arXiv:2205.09546v1 [stat.ML])

In this work, we provide an exact likelihood alternative to the variational
training of generative autoencoders. We show that VAE-style autoencoders can be
constructed using invertible layers, which offer a tractable exact likelihood
without the need for any regularization terms. This is achieved while leaving
complete freedom in the choice of encoder, decoder and prior architectures,
making our approach a drop-in replacement for the training of existing VAEs and
VAE-style models. We refer to the resulting models as Autoencoders within …

arxiv gap likelihood ml training