Glow: Generative Flow with Invertible 1×1 Convolutions

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Authors Diederik P. Kingma, Prafulla Dhariwal
Journal/Conference Name NeurIPS 2018 12
Paper Category
Paper 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
Date of publication 2018
Code Programming Language Multiple
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