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arXiv · 1705.06830

Exploring the structure of a real-time, arbitrary neural artistic stylization network

Golnaz Ghiasi·Honglak Lee·Manjunath Kudlur·Vincent Dumoulin·Jonathon Shlens
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Abstract

In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer networks by learning to predict the conditional instance normalization parameters directly from a style image. The model is successfully trained on a corpus of roughly 80,000 paintings and is able to generalize to paintings previously unobserved. We demonstrate that the learned embedding space is smooth and contains a rich structure and organizes semantic information associated with paintings in an entirely unsupervised manner.

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Golnaz Ghiasi, Honglak Lee, Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens. 2017-08-24. Exploring the structure of a real-time, arbitrary neural artistic stylization network. https://arxiv.org/abs/1705.06830

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