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Multiple Style Transfer Via Variational Autoencoder

Abstract : Modern works on style transfer focus on transferring style from a single image. Recently, some approaches study multiple style transfer; these, however, are either too slow or fail to mix multiple styles. We propose ST-VAE, a Variational AutoEncoder for latent space-based style transfer. It performs multiple style transfer by projecting nonlinear styles to a linear latent space, enabling to merge styles via linear interpolation before transferring the new style to the content image. To evaluate ST-VAE, we experiment on COCO for single and multiple style transfer. We also present a case study revealing that ST-VAE outperforms other methods while being faster, flexible, and setting a new path for multiple style transfer.
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Contributor : Vicky Kalogeiton Connect in order to contact the contributor
Submitted on : Friday, September 24, 2021 - 4:46:53 PM
Last modification on : Thursday, September 30, 2021 - 11:27:21 AM


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Zhi-Song Liu, Vicky Kalogeiton, Marie-Paule Cani. Multiple Style Transfer Via Variational Autoencoder. 2021 IEEE International Conference on Image Processing (ICIP), Sep 2021, Anchorage, Alaska (virtual), United States. ⟨10.1109/ICIP42928.2021.9506379⟩. ⟨hal-03353538⟩



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