The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Neural Style Transfer: A Review
Open accessAbstract
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at: https://osf.io/f8tu4/.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“time exploit bilateral [19] and difference of Gaussians filters [20] to automatically produce cartoon-like effects.”
BackgroundIntriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Understanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
Portilla, J., & Simoncelli, E. P. (2000) · International Journal of Computer Vision · doi:10.1023/a:1026553619983 · Cited by 1,806
Adaptive batch normalization for practical domain adaptation
Li, Y., Wang, N., Shi, J., Hou, X., & Liu, J. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.03.005 · Cited by 601
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Ghiasi, G., Lee, H., Kudlur, M., Dumoulin, V., & Shlens, J. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.114 · Cited by 306
Cited by
Understanding and creating art with AI: Review and outlook
Cetinic, E., & She, J. (2022) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3475799 · Cited by 579
Bayoudh, K., Knani, R., Hamdaoui, F., & Mtibaa, A. (2022) · The Visual Computer · doi:10.1007/s00371-021-02166-7 · Cited by 456
AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer
Liu, S., Lin, T., He, D., Li, F., Wang, M., Li, X., Sun, Z., Li, Q., & Ding, E. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00658 · Cited by 420
Conwell, C., Prince, J. S., Kay, K. N., Alvarez, G. A., & Konkle, T. (2022) · doi:10.1101/2022.03.28.485868 · Cited by 160
Splicing ViT Features for Semantic Appearance Transfer
Tumanyan, N., Bar-Tal, O., Bagon, S., & Dekel, T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01048 · Cited by 153
Metadata
- Authors
- Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., & Song, M.
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2020-11-01
- Volume
- 26
- Issue
- 11
- Pages
- 3365-3385
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W2962750014
- Cited by
- 856
- References
- 11
- FWCI
- 42.12
- Type
- journal article
- Language
- en
- Open access
- green
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- Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., & Song, M. (2020). Neural Style Transfer: A Review. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/tvcg.2019.2921336
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