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BiTGAN: bilateral generative adversarial networks for Chinese ink wash painting style transfer

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References

  1. He B, Gao F, Ma D, et al. ChipGAN: a generative adversarial network for Chinese ink wash painting style transfer. In: Proceedings of the 26th ACM International Conference on Multimedia, 2018. 1172–1180

  2. Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 2223–2232

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  4. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016. 770–778

  5. Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In: Proceedings of European Conference on Computer Vision, Cham, 2014. 818–833

  6. Huang X, Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, 2017. 1501–1510

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Acknowledgements

This work was supported in part by National Key Research and Development Program of China (Grant No. 2018AAA0103202) and National Natural Science Foundation of China (Grant Nos. 62106184, 62036007, 61922066, 61876142, 62176198).

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Authors and Affiliations

  1. State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, 710071, China

    Xiao He, Mingrui Zhu & Nannan Wang

  2. The Chinese University of Hong Kong (Shenzhen), Shenzhen, 518172, China

    Xiaoyu Wang

  3. Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Xinbo Gao

Authors
  1. Xiao He
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  2. Mingrui Zhu
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  3. Nannan Wang
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  4. Xiaoyu Wang
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  5. Xinbo Gao
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Corresponding authors

Correspondence to Mingrui Zhu or Nannan Wang.

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Supporting information

Appendixes A–C. The supporting information is available online at info.scichina.com and link.springer.com. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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He, X., Zhu, M., Wang, N. et al. BiTGAN: bilateral generative adversarial networks for Chinese ink wash painting style transfer. Sci. China Inf. Sci. 66, 119104 (2023). https://doi.org/10.1007/s11432-022-3541-x

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