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Automatic generation of high-quality Chinese fonts from a few online training samples is a challenging task, especially when the amount of samples is very small.
Visualizing data using t-sne,
L. v. d. Maaten, G. Hinton, · 2008
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Learning a manifold of fonts,
N. D. Campbell, J. Kautz, · 2014
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Strokebank: Automating personalized chinese handwriting generation,
A. Zong, Y. Zhu, · 2014
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Very deep convolutional networks for large-scale image recognition,
K. Simonyan, A. Zisserman, · 2014
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Recurrent net dreams up fake chinese characters in vector format with tensorflow,
D. Ha, · 2015
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An arabic handwriting synthesis system,
Y. Elarian, I. Ahmad, S. Awaida, W. G. Al-Khatib, A. Zidouri, · 2015
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Rewrite: Neural style transfer for chinese fonts, 2016,
Y. Tian, · 2016
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Perceptual losses for real-time style transfer and super-resolution,
J. Johnson, A. Alahi, L. Fei-Fei, · 2016
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zi2zi: Master chinese calligraphy with conditional adversarial networks, 2017,
Y. Tian, · 2017
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Dcfont: an end-to-end deep chinese font generation system,
Y. Jiang, Z. Lian, Y. Tang, J. Xiao, · 2017
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Image-to-image translation with conditional adversarial networks,
P. Isola, J.-Y. Zhu, T. Zhou, A. A. Efros, · 2017
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Learning to write stylized chinese characters by reading a handful of examples,
D. Sun, T. Ren, C. Li, H. Su, J. Zhu, · 2017
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Drawing and recognizing chinese characters with recurrent neural network,
X.-Y. Zhang, F. Yin, Y.-M. Zhang, C.-L. Liu, Y. Bengio, · 2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium,
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, S. Hochreiter, · 2017
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Easyfont: a style learning-based system to easily build your large-scale handwriting fonts,
Z. Lian, B. Zhao, X. Chen, J. Xiao, · 2018
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Separating style and content for generalized style transfer,
Y. Zhang, Y. Zhang, W. Cai, · 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, T. Darrell, · 2018
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Scaling neural machine translation,
M. Ott, S. Edunov, D. Grangier, M. Auli, · 2018
Cited alongside, same era.
The contextual loss for image transformation with non-aligned data,
R. Mechrez, I. Talmi, L. Zelnik-Manor, · 2018
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Scfont: Structure-guided chinese font generation via deep stacked networks (2019)
Y. Jiang, Z. Lian, Y. Tang, J. Xiao, · 2019
Cited alongside, same era.
Fontrnn: Generating large-scale chinese fonts via recurrent neural network,
S. Tang, Z. Xia, Z. Lian, Y. Tang, J. Xiao, · 2019
Cited alongside, same era.
Automatic generation of chinese vector fonts via deep layout inferring,
Y. Gao, Z. Lian, Y. Tang, J. Xiao, · 2019
Cited alongside, same era.
Artistic glyph image synthesis via one-stage few-shot learning,
End-to-end object detection with transformers,
N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, S. Zagoruyko, · 2020
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Informer: Beyond efficient transformer for long sequence time-series forecasting,
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, W. Zhang, · 2020
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Few-shot compositional font generation with dual memory,
J. Cha, S. Chun, G. Lee, B. Lee, S. Kim, H. Lee, · 2020
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Fontrl: Chinese font synthesis via deep reinforcement learning,
Y. Liu, Z. Lian, · 2021
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Strokegan: Reducing mode collapse in chinese font generation via stroke encoding,
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Y. Gao, Y. Guo, Z. Lian, Y. Tang, J. Xiao, · 2019
Cited alongside, same era.
Text generation from knowledge graphs with graph transformers,
R. Koncel-Kedziorski, D. Bekal, Y. Luan, M. Lapata, H. Hajishirzi, · 2019
Cited alongside, same era.
Hierarchical transformers for long document classification,
R. Pappagari, P. Zelasko, J. Villalba, Y. Carmiel, N. Dehak, · 2019
Cited alongside, same era.
Transformer-based neural network for answer selection in question answering,
T. Shao, Y. Guo, H. Chen, Z. Hao, · 2019
Cited alongside, same era.
Self-attention generative adversarial networks,
H. Zhang, I. Goodfellow, D. Metaxas, A. Odena, · 2019
Cited alongside, same era.
Calligan: Style and structure-aware chinese calligraphy character generator,
S.-J. Wu, C.-Y. Yang, J. Y.-j. Hsu, · 2020
Cited alongside, same era.
Gan-based unpaired chinese character image translation via skeleton transformation and stroke rendering,
Y. Gao, J. Wu, · 2020
Cited alongside, same era.
J. Zeng, Q. Chen, Y. Liu, M. Wang, Y. Yao, · 2021
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Zigan: Fine-grained chinese calligraphy font generation via a few-shot style transfer approach,
Q. Wen, S. Li, B. Han, Y. Yuan, · 2021
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Dg-font: Deformable generative networks for unsupervised font generation,
Y. Xie, X. Chen, L. Sun, Y. Lu, · 2021
Later among the works it cites.
Few-shot font style transfer between different languages,
C. Li, Y. Taniguchi, M. Lu, S. Konomi, · 2021
Later among the works it cites.
Multiple heads are better than one: Few-shot font generation with multiple localized experts,
S. Park, S. Chun, J. Cha, B. Lee, H. Shim, · 2021
Later among the works it cites.
Generalized pyramid co-attention with learnable aggregation net for video question answering,
L. Gao, T. Chen, X. Li, P. Zeng, L. Zhao, Y.-F. Li, · 2021
Later among the works it cites.
Bottleneck transformers for visual recognition,
A. Srinivas, T.-Y. Lin, N. Parmar, J. Shlens, P. Abbeel, A. Vaswani, · 2021
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Localvit: Bringing locality to vision transformers,
Y. Li, K. Zhang, J. Cao, R. Timofte, L. Van Gool, · 2021
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Cvt: Introducing convolutions to vision transformers,
H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, L. Zhang, · 2021
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Xmp-font: Self-supervised cross-modality pre-training for few-shot font generation,
W. Liu, F. Liu, F. Din, Q. He, Z. Yi, · 2022
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Pay attention to what you read: Non-recurrent handwritten text-line recognition,
L. Kang, P. Riba, M. Rusiñol, A. Fornés, M. Villegas, · 2022
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Transformer-based approach for joint handwriting and named entity recognition in historical document,
A. C. Rouhou, M. Dhiaf, Y. Kessentini, S. B. Salem, · 2022
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