Arbitrary style transfer via content consistency and style consistency
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software
Link
https://link.springer.com/content/pdf/10.1007/s00371-023-02855-5.pdf
Reference38 articles.
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2. Lin, T., Ma, Z., Li, F., He, D., Li, X., Ding, E., Wang, N., Li, J., Gao, X.: Drafting and revision: Laplacian pyramid network for fast high-quality artistic style transfer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5141–5150 (2021)
3. Ma, Z., Lin, T., Li, X., Li, F., He, D., Ding, E., Wang, N., Gao, X.: Dual-affinity style embedding network for semantic-aligned image style transfer. IEEE Trans. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3143356
4. Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. IEEE Trans. Vis. Comput. Graph. 26(11), 3365–3385 (2019)
5. Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694–711. Springer (2016)
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