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.

Why do cats' eyes glow in the dark? That eerie glow is actually a pair of built-in night-vision goggles. Cat eyes have even inspired some life-saving tech. Breakthroughs, discoveries, and DIY tips sent six days a week. One foggy night in 1933, a businessman named Percy Shaw was driving home from the pub in Yorkshire, England.

Unsupervised learning of probabilistic models is a central yet challenging problem. Deep generative models have shown promising results in modeling complex distributions such as natural images (Radford et al.,2015), audio (Van Den Oord et al.,2016)and text (Bowman et al.,2015).

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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.

We will incorporate the answers/other feedback into the revised manuscript. The general quality of samples seems to be negatively impacted. We agree other wavelets could be potentially interesting. SR is not claimed as our primary goal/contribution. Rather, it is a fortuitous byproduct of the conditional structure that WF enables. A more thorough exploration of WF for SR is a promising direction for future work.