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Gatys et al.
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Microsoft coco: Common objects in context
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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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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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Batch normalized recurrent neural networks
C. Laurent, G. Pereyra, P. Brakel, Y. Zhang, and Y. Bengio · 2016
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Combining markov random fields and convolutional neural networks for image synthesis
C. Li and M. Wand · 2016
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Precomputed real-time texture synthesis with markovian generative adversarial networks
C. Li and M. Wand · 2016
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Revisiting batch normalization for practical domain adaptation
M. Arjovsky, S. Chintala, and L. Bottou · 2017
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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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Recurrent batch normalization
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A learned representation for artistic style
V. Dumoulin, J. Shlens, and M. Kudlur · 2017
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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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Stacked generative adversarial networks
X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie · 2017
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Y. Li, N. Wang, J. Shi, J. Liu, and X. Hou · 2016
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Q. Liao, K. Kawaguchi, and T. Poggio · 2016
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Painter by numbers, wikiart
K. Nichol · 2016
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Pixel recurrent neural networks
A. v. d. Oord, N. Kalchbrenner, and K. Kavukcuoglu · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Generative adversarial text to image synthesis
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee · 2016
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Batch renormalization: Towards reducing minibatch dependence in batch-normalized models
S. Ioffe · 2017
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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Learning to discover cross-domain relations with generative adversarial networks
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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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Demystifying neural style transfer
Y. Li, N. Wang, J. Liu, and X. Hou · 2017
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Unsupervised image-to-image translation networks
M.-Y. Liu, T. Breuel, and J. Kautz · 2017
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Synthetic to real adaptation with deep generative correlation alignment networks
X. Peng and K. Saenko · 2017
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Normalizing the normalizers: Comparing and extending network normalization schemes
M. Ren, R. Liao, R. Urtasun, F. H. Sinz, and R. S. Zemel · 2017
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Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 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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Stable and controllable neural texture synthesis and style transfer using histogram losses
P. Wilmot, E. Risser, and C. Barnes · 2017
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Multi-style generative network for real-time transfer
H. Zhang and K. Dana · 2017
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