Domain-Specific Mappings for Generative Adversarial Style Transfer

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12353)


Style transfer generates an image whose content comes from one image and style from the other. Image-to-image translation approaches with disentangled representations have been shown effective for style transfer between two image categories. However, previous methods often assume a shared domain-invariant content space, which could compromise the content representation power. For addressing this issue, this paper leverages domain-specific mappings for remapping latent features in the shared content space to domain-specific content spaces. This way, images can be encoded more properly for style transfer. Experiments show that the proposed method outperforms previous style transfer methods, particularly on challenging scenarios that would require semantic correspondences between images. Code and results are available at



This work was supported in part by MOST under grant 107-2221-E-002-147-MY3 and MOST Joint Research Center for AI Technology and All Vista Healthcare under grant 109-2634-F-002-032.

Supplementary material

504445_1_En_34_MOESM1_ESM.pdf (39.2 mb)
Supplementary material 1 (pdf 40123 KB)


  1. 1.
    Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: StyleBank: an explicit representation for neural image style transfer. In: CVPR (2017)Google Scholar
  2. 2.
    Cho, W., Choi, S., Keetae Park, D., Shin, I., Choo, J.: Image-to-image translation via group-wise deep whitening-and-coloring transformation. In: CVPR (2019)Google Scholar
  3. 3.
    Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: CVPR (2018)Google Scholar
  4. 4.
    Gatys, L., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: NeurIPS (2015)Google Scholar
  5. 5.
    Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR (2016)Google Scholar
  6. 6.
    Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)Google Scholar
  7. 7.
    Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: ICCV (2017)Google Scholar
  8. 8.
    Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179–196. Springer, Cham (2018). Scholar
  9. 9.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)Google Scholar
  10. 10.
    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 694–711. Springer, Cham (2016). Scholar
  11. 11.
    Kim, J., Kim, M., Kang, H., Lee, K.: U-GAT-IT: unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation. arXiv preprint arXiv:1907.10830 (2019)
  12. 12.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
  13. 13.
    Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M., Yang, M.-H.: Diverse image-to-image translation via disentangled representations. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 36–52. Springer, Cham (2018). Scholar
  14. 14.
    Li, X., et al.: Attribute guided unpaired image-to-image translation with semi-supervised learning. arXiv preprint arXiv:1904.12428 (2019)
  15. 15.
    Li, X., Liu, S., Kautz, J., Yang, M.H.: Learning linear transformations for fast arbitrary style transfer. arXiv preprint arXiv:1808.04537 (2018)
  16. 16.
    Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Universal style transfer via feature transforms. In: NeurIPS (2017)Google Scholar
  17. 17.
    Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. In: ACM SIGGRAPH (2017)Google Scholar
  18. 18.
    Lin, J., Pang, Y., Xia, Y., Chen, Z., Luo, J.: TuiGAN: learning versatile image-to-image translation with two unpaired images. ECCV (2020)Google Scholar
  19. 19.
    Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: NeurIPS (2017)Google Scholar
  20. 20.
    Liu, M.Y., et al.: Few-shot unsupervised image-to-image translation. In: ICCV (2019)Google Scholar
  21. 21.
    Lu, M., Zhao, H., Yao, A., Xu, F., Chen, Y., Zhang, L.: Decoder network over lightweight reconstructed feature for fast semantic style transfer. In: ICCV (2017)Google Scholar
  22. 22.
    Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: CVPR (2017)Google Scholar
  23. 23.
    Mao, Q., Lee, H.Y., Tseng, H.Y., Ma, S., Yang, M.H.: Mode seeking generative adversarial networks for diverse image synthesis. In: CVPR (2019)Google Scholar
  24. 24.
    Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: ICCV (2017)Google Scholar
  25. 25.
    Mejjati, Y.A., Richardt, C., Tompkin, J., Cosker, D., Kim, K.I.: Unsupervised attention-guided image-to-image translation. In: NeurIPS (2018)Google Scholar
  26. 26.
    Mo, S., Cho, M., Shin, J.: InstaGAN: instance-aware image-to-image translation. arXiv preprint arXiv:1812.10889 (2018)
  27. 27.
    Pumarola, A., Agudo, A., Martinez, A.M., Sanfeliu, A., Moreno-Noguer, F.: GANimation: anatomically-aware facial animation from a single image. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 835–851. Springer, Cham (2018). Scholar
  28. 28.
    Wu, W., Cao, K., Li, C., Qian, C., Loy, C.C.: TransGaGa: geometry-aware unsupervised image-to-image translation. arXiv preprint arXiv:1904.09571 (2019)
  29. 29.
    Xiao, T., Hong, J., Ma, J.: ELEGANT: exchanging latent encodings with GAN for transferring multiple face attributes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11214, pp. 172–187. Springer, Cham (2018). Scholar
  30. 30.
    Yi, Z., Zhang, H., Tan, P., Gong, M.: DualGAN: unsupervised dual learning for image-to-image translation. In: ICCV (2017)Google Scholar
  31. 31.
    Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)Google Scholar
  32. 32.
    Zheng, Z., Yu, Z., Zheng, H., Wu, Y., Zheng, B., Lin, P.: Generative adversarial network with multi-branch discriminator for cross-species image-to-image translation. arXiv preprint arXiv:1901.10895 (2019)
  33. 33.
    Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)Google Scholar
  34. 34.
    Zhu, J.Y., et al.: Toward multimodal image-to-image translation. In: NeurIPS (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.National Taiwan UniversityTaipeiTaiwan

Personalised recommendations