Variational Autoencoders with Normalizing Flow Decoders

Abstract

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.

References in corpus (5)

• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Variational Lossy Autoencoder
• PixelVAE: A Latent Variable Model for Natural Images
• Learnable Explicit Density for Continuous Latent Space and Variational Inference

Cited by in corpus (2)

• Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
• Generative networks as inverse problems with fractional wavelet scattering networks