Title: Glow: Generative Flow with Invertible 1x1 Convolutions,
Abstract: Flow-based generative models (Dinh et al., 2014) are conceptually attractive
due to tractability of the exact log-likelihood, tractability of exact
latent-variable inference, and parallelizability of both training and
synthesis. In this paper we propose Glow, a simple type of generative flow
using an invertible 1x1 convolution. Using our method we demonstrate a
significant improvement in log-likelihood on standard benchmarks. Perhaps most
strikingly, we demonstrate that a generative model optimized towards the plain
log-likelihood objective is capable of efficient realistic-looking synthesis
and manipulation of large images. The code for our model is available at
this https URL