Glow: Generative Flow with Invertible 1x1 Convolutions
NeurIPS, pp. 10215-10224, 2018.
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...More
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