Glow: Generative Flow with Invertible 1x1 Convolutions

07/09/2018

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by   Diederik P. Kingma, et al.

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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 <i>Glow</i>, 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 <a href="https://github.com/openai/glow">https://github.com/openai/glow</a>