%0 Journal Article %1 kingma2018generative %A Kingma, Diederik P. %A Dhariwal, Prafulla %D 2018 %K flows generative-models %T Glow: Generative Flow with Invertible 1x1 Convolutions %U http://arxiv.org/abs/1807.03039 %X 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

@article{kingma2018generative, 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}, added-at = {2019-12-09T11:32:02.000+0100}, author = {Kingma, Diederik P. and Dhariwal, Prafulla}, biburl = {https://www.bibsonomy.org/bibtex/267b6df05ed48ddb6dc9fb6fc3247bc84/kirk86}, description = {[1807.03039] Glow: Generative Flow with Invertible 1x1 Convolutions}, interhash = {5c6e8244f0febbc1c7d1906c844d95e8}, intrahash = {67b6df05ed48ddb6dc9fb6fc3247bc84}, keywords = {flows generative-models}, note = {cite arxiv:1807.03039Comment: 15 pages; fixed typo in abstract}, timestamp = {2019-12-09T11:32:02.000+0100}, title = {Glow: Generative Flow with Invertible 1x1 Convolutions}, url = {http://arxiv.org/abs/1807.03039}, year = 2018 }