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We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer.
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Chiu, T.Y., Gurari, D.: Iterative feature transformation for fast and versatile universal style transfer. In: Proceedings of European Conference on Computer Vision. pp. 169–184 (2020)
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Texler, O., Futschik, D., Fišer, J., Lukáč, M., Lu, J., Shechtman, E., Sýkora, D.: Arbitrary style transfer using neurally-guided patch-based synthesis. Computers & Graphics 87
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An, J., Huang, S., Song, Y., Dou, D., Liu, W., Luo, J.: ArtFlow: Unbiased image style transfer via reversible neural flows. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 862–871 (2021)
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