Structure-Preserving Neural Style Transfer

Ming-Ming Cheng; Xiao-Chang Liu; Jie Wang; Shao-Ping Lu; Yu-Kun Lai; Paul L. Rosin

doi:10.1109/tip.2019.2936746

journal article Jan 01, 2020

Structure-Preserving Neural Style Transfer

Ming-Ming Cheng

Xiao-Chang Liu Jie Wang

Shao-Ping Lu

Yu-Kun Lai

Paul L. Rosin

IEEE Transactions on Image Processing Vol. 29 pp. 909-920 · Institute of Electrical and Electronics Engineers (IEEE)

View at Publisher Save 10.1109/tip.2019.2936746

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References

47

[1]

Depth estimation from image structure

A. Torralba, A. Oliva

IEEE Transactions on Pattern Analysis and Machine... 10.1109/tpami.2002.1033214

[2]

10.1145/3145749.3149449

[3]

10.1109/cvpr.2015.7299155

[4]

10.1109/cvpr.2017.740

[5]

10.1109/tpami.2018.2878849

[6]

10.1145/3092919.3092924

[7]

simonyan "Very deep convolutional networks for large-scale image recognition" Proc Int Conf Learn Represent (2015)

[8]

simonyan "Deep inside convolutional networks: Visualising image classification models and saliency maps" Proc Int Conf Learn Represent (2013)

[9]

10.1023/a:1026553619983

[10]

10.5244/c.31.153

[11]

10.1109/cvpr.2016.265

[12]

ulyanov "Texture networks: Feed-forward synthesis of textures and stylized images" Proc 33rd Int Conf Mach Learn (2016)

[13]

goodfellow "Generative adversarial nets" Proc Adv Neural Inf Process Syst (2014)

[14]

10.1109/iccv.2017.438

[15]

10.1145/383259.383327

[16]

heeger "Pyramid-based texture analysis/synthesis" Proc 22nd Annu Conf Comput Graph Interact Techn (1995)

[17]

10.1145/383259.383295

[18]

10.1109/tpami.2018.2815688

[19]

Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization

Xun Huang, Serge Belongie

2017 IEEE International Conference on Computer Vis... 10.1109/iccv.2017.167

[20]

iandola "SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5MB model size" arXiv 1602 07360 (2016)

[21]

10.1109/tvcg.2019.2921336

[22]

10.24963/ijcai.2017/310

[23]

dumoulin "A learned representation for artistic style" Proc Int Conf Learn Represent (2017)

[24]

10.1007/978-3-030-01219-9_28

[25]

chen "Single-image depth perception in the wild" Proc Adv Neural Inf Process Syst (2016)

[26]

10.1109/iccv.1999.790383

[27]

lin "Microsoft COCO: Common objects in context" Proc Eur Conf Comput Vis (2014)

[28]

Image quilting for texture synthesis and transfer

Alexei A. Efros, William T. Freeman

Proceedings of the 28th annual conference on Compu... 10.1145/383259.383296

[29]

gatys "Preserving color in neural artistic style transfer" arXiv 1606 05897 (2016)

[30]

gatys "Texture synthesis using convolutional neural networks" Proc Adv Neural Inf Process Syst (2015)

[31]

chen "Fast patch-based style transfer of arbitrary style" Proc Adv Neural Inf Process Syst (2016)

[32]

gatys "A neural algorithm of artistic style" arXiv 1508 06576 (2015)

[33]

10.1109/cvpr.2017.296

[34]

zhang "Multi-style generative network for real-time transfer" arXiv 1703 06953 (2017)

[35]

10.1007/978-3-319-46475-6_43

[36]

10.1145/1268517.1268527

[37]

kingma "Adam: A method for stochastic optimization" Proc Int Conf Learn Represent (2014)

[38]

10.1109/iccv.2017.244

[39]

10.1109/tit.1962.1057698

[40]

Image quality assessment: from error visibility to structural similarity

Zhou Wang, A.C. Bovik, H.R. Sheikh et al.

IEEE Transactions on Image Processing 10.1109/tip.2003.819861

[41]

10.1109/cvpr.2016.272

[42]

10.1109/cvpr.2017.437

[43]

krizhevsky "ImageNet classification with deep convolutional neural networks" Proc 25th Int Conf Neural Inf Process Syst (2012)

[44]

Holistically-Nested Edge Detection

Saining Xie, Zhuowen Tu

International Journal of Computer Vision 10.1007/s11263-017-1004-z

[45]

10.1109/cvpr.2017.36

[46]

wilmot "Stable and controllable neural texture synthesis and style transfer using histogram losses" arXiv 1701 08893 (2017)

[47]

10.1007/978-3-319-46487-9_43

Metrics

81

Citations

47

References

Details

Published: Jan 01, 2020
Vol/Issue: 29
Pages: 909-920
License: View

Authors

M

Ming-Ming Cheng

College of Computer Science, Nankai University, Tianjin, China

China National GeneBank, Beijing Genomics Institute (BGI)–Shenzhen, Shenzhen, 518083, China.; BGI Education Center,University of Chinese Academy of Sciences,Shenzhen, 518083, China.

Funding

National Natural Science Foundation of China Award: 61572264

National Youth Talent Support Program

Natural Science Foundation of Tianjin City Award: 17JCJQJC43700

Cite This Article

Ming-Ming Cheng, Xiao-Chang Liu, Jie Wang, et al. (2020). Structure-Preserving Neural Style Transfer. IEEE Transactions on Image Processing, 29, 909-920. https://doi.org/10.1109/tip.2019.2936746

Structure-Preserving Neural Style Transfer

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