1 research outputs found
Variational Autoencoders with Normalizing Flow Decoders
Recently proposed normalizing flow models such as Glow have been shown to be
able to generate high quality, high dimensional images with relatively fast
sampling speed. Due to their inherently restrictive architecture, however, it
is necessary that they are excessively deep in order to train effectively. In
this paper we propose to combine Glow with an underlying variational
autoencoder in order to counteract this issue. We demonstrate that our proposed
model is competitive with Glow in terms of image quality and test likelihood
while requiring far less time for training