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Closing the gap: Exact maximum likelihood training of generative autoencoders using invertible layers. (arXiv:2205.09546v2 [stat.ML] UPDATED)
Aug. 2, 2022, 2:12 a.m. | Gianluigi Silvestri, Daan Roos, Luca Ambrogioni
stat.ML updates on arXiv.org arxiv.org
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 …
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