Glow: Generative Flow with Invertible 1x1 Convolutions

Part of Advances in Neural Information Processing Systems 31 (NeurIPS 2018)

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Durk P. Kingma, Prafulla Dhariwal


Flow-based generative models 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 invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood and qualitative sample quality. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient synthesis of large and subjectively realistic-looking images.