“Glow: Generative Flow With Invertible 1×1 Convolutions”, Diederik P. Kingma, Prafulla Dhariwal2018-07-09 (CNN; backlinks; similar)⁠:

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 1×1 convolution.

Using our method we demonstrate an 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.

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