Title: Glow: Generative Flow with Invertible 1x1 Convolutions
Authors: Diederik P. Kingma, Prafulla Dhariwal
Published: 9th July 2018 (Monday) @ 10:57:26
Link: http://arxiv.org/abs/1807.03039v2
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 https://github.com/openai/glow