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arXiv · 1901.11137

Emerging Convolutions for Generative Normalizing Flows

Emiel Hoogeboom·Rianne van den Berg·Max Welling
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Abstract

Generative flows are attractive because they admit exact likelihood optimization and efficient image synthesis. Recently, Kingma & Dhariwal (2018) demonstrated with Glow that generative flows are capable of generating high quality images. We generalize the 1 x 1 convolutions proposed in Glow to invertible d x d convolutions, which are more flexible since they operate on both channel and spatial axes. We propose two methods to produce invertible convolutions that have receptive fields identical to standard convolutions: Emerging convolutions are obtained by chaining specific autoregressive convolutions, and periodic convolutions are decoupled in the frequency domain. Our experiments show that the flexibility of d x d convolutions significantly improves the performance of generative flow models on galaxy images, CIFAR10 and ImageNet.

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Emiel Hoogeboom, Rianne van den Berg, Max Welling. 2019-05-20. Emerging Convolutions for Generative Normalizing Flows. https://arxiv.org/abs/1901.11137

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