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Automatic character generation is an appealing solution for new typeface design, especially for Chinese typefaces including over 3700 most commonly-used characters.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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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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From a to z: supervised transfer of style and content using deep neural network generators
P. Upchurch, N. Snavely, and K. Bala · 2016
Earlier work this paper cites.
Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
Earlier work this paper cites.
Handwritten chinese character recognition with spatial transformer and deep residual networks
Z. Zhong, X.-Y. Zhang, F. Yin, and C.-L. Liu · 2016
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Unsupervised diverse colorization via generative adversarial networks
Y. Cao, Z. Zhou, W. Zhang, and Y. Yu · 2017
Cited alongside, same era.
Stylebank: An explicit representation for neural image style transfer
D. Chen, L. Yuan, J. Liao, N. Yu, and G. Hua · 2017
Cited alongside, same era.
Dcfont: an end-to-end deep chinese font generation system
Y. Jiang, Z. Lian, Y. Tang, and J. Xiao · 2017
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Cited alongside, same era.
Auto-encoder guided gan for chinese calligraphy synthesis
P. Lyu, X. Bai, C. Yao, Z. Zhu, T. Huang, and W. Liu · 2017
Cited alongside, same era.
Learning to write stylized chinese characters by reading a handful of examples
Ntire 2018 challenge on image dehazing: Methods and results
C. Ancuti, C. O. Ancuti, R. Timofte, L. Van Gool, L. Zhang, M.-H. Yang, V. M. Patel, H. Zhang, V. A. Sindagi, R. Zhao, et al · 2018
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Multi-content gan for few-shot font style transfer
S. Azadi, M. Fisher, V. G. Kim, Z. Wang, E. Shechtman, and T. Darrell · 2018
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Pairedcyclegan: Asymmetric style transfer for applying and removing makeup
H. Chang, J. Lu, F. Yu, and A. Finkelstein · 2018
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Chinese handwriting imitation with hierarchical generative adversarial network
J. Chang, Y. Gu, Y. Zhang, and Y. Wang · 2018
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Cartoongan: Generative adversarial networks for photo cartoonization
Y. Chen, Y.-K. Lai, and Y.-J. Liu · 2018
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A style-aware content loss for real-time hd style transfer
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D. Sun, T. Ren, C. Li, J. Zhu, and H. Su · 2017
Cited alongside, same era.
Building fast and compact convolutional neural networks for offline handwritten chinese character recognition
X. Xiao, L. Jin, Y. Yang, W. Yang, J. Sun, and T. Chang · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
Cited alongside, same era.
A. Sanakoyeu, D. Kotovenko, S. Lang, and B. Ommer · 2018
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Learning discriminative video representations using adversarial perturbations
J. Wang and A. Cherian · 2018
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Separating style and content for generalized style transfer
Y. Zhang, Y. Zhang, and W. Cai · 2018
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