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Universal style transfer aims to transfer arbitrary visual styles to content images.
Image analogies
A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin · 2001
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Digital image processing (3rd edition)
R. C. Gonzalez and R. E. Woods · 2008
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Data-driven hallucination of different times of day from a single outdoor photo
Y. Shih, S. Paris, F. Durand, and W. T. Freeman · 2013
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Recognizing image style
S. Karayev, M. Trentacoste, H. Han, A. Agarwala, T. Darrell, A. Hertzmann, and H. Winnemoeller · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Style transfer for headshot portraits
Y. Shih, S. Paris, C. Barnes, W. T. Freeman, and F. Durand · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Texture synthesis using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2015
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Semantic style transfer and turning two-bit doodles into fine artworks
A. J. Champandard · 2016
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Fast patch-based style transfer of arbitrary style
T. Q. Chen and M. Schmidt · 2016
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Generating images with perceptual similarity metrics based on deep networks
A. Dosovitskiy and T. Brox · 2016
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Split and match: Example-based adaptive patch sampling for unsupervised style transfer
O. Frigo, N. Sabater, J. Delon, and P. Hellier · 2016
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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Whitening and coloring transforms for multivariate gaussian random variables
M. Hossain · 2016
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Combining markov random fields and convolutional neural networks for image synthesis
C. Li and M. Wand · 2016
Controlling perceptual factors in neural style transfer
L. A. Gatys, A. S. Ecker, M. Bethge, A. Hertzmann, and E. Shechtman · 2017
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Exploring the structure of a real-time, arbitrary neural artistic stylization network
G. Ghiasi, H. Lee, M. Kudlur, V. Dumoulin, and J. Shlens · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. Belongie · 2017
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Diversified texture synthesis with feed-forward networks
Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu, and M.-H. Yang · 2017
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Visual attribute transfer through deep image analogy
J. Liao, Y. Yao, L. Yuan, G. Hua, and S. B. Kang · 2017
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Deep photo style transfer
F. Luan, S. Paris, E. Shechtman, and K. Bala · 2017
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Precomputed real-time texture synthesis with markovian generative adversarial networks
C. Li and M. Wand · 2016
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Texture networks: Feed-forward synthesis of textures and stylized images
D. Ulyanov, V. Lebedev, A. Vedaldi, and V. Lempitsky · 2016
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Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
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Stylebank: An explicit representation for neural image style transfer
D. Chen, L. Yuan, J. Liao, N. Yu, and G. Hua · 2017
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A learned representation for artistic style
V. Dumoulin, J. Shlens, and M. Kudlur · 2017
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Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2017
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Zm-net: Real-time zero-shot image manipulation network
H. Wang, X. Liang, H. Zhang, D.-Y. Yeung, and E. P. Xing · 2017
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Multimodal transfer: A hierarchical deep convolutional neural network for fast artistic style transfer
X. Wang, G. Oxholm, D. Zhang, and Y.-F. Wang · 2017
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Stable and controllable neural texture synthesis and style transfer using histogram losses
P. Wilmot, E. Risser, and C. Barnes · 2017
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