Fetching the paper…
Reading the bibliography…
The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time.
Julesz, B., et al.: Textons, the elements of texture perception, and their interactions. Nature 290
1981
Earlier work this paper cites.
Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. pp. 341–346. ACM (2001)
2001
Earlier work this paper cites.
Gooch, B., Gooch, A.: Non-photorealistic rendering. A. K. Peters, Ltd., Natick, MA, USA (2001)
2001
Earlier work this paper cites.
Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Proceedings of the 28th annual conference on Computer graphics and interactive techniques. pp. 327–340. ACM (2001)
2001
Earlier work this paper cites.
Strothotte, T., Schlechtweg, S.: Non-photorealistic computer graphics: modeling, rendering, and animation. Morgan Kaufmann (2002)
2002
Earlier work this paper cites.
Zhu, S.C., Guo, C.E., Wang, Y., Xu, Z.: What are textons? International Journal of Computer Vision 62
2005
Earlier work this paper cites.
Rosin, P., Collomosse, J.: Image and video-based artistic stylisation, vol. 42. Springer Science & Business Media (2012)
2012
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Gatys, L.A., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: Advances in Neural Information Processing Systems. pp. 262–270 (2015)
2015
Earlier work this paper cites.
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Advances in Neural Information Processing Systems. pp. 658–666 (2016)
2016
Earlier work this paper cites.
Frigo, O., Sabater, N., Delon, J., Hellier, P.: Split and match: Example-based adaptive patch sampling for unsupervised style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 553–561 (2016)
2016
Earlier work this paper cites.
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2414–2423 (2016)
2016
Earlier work this paper cites.
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 (2016)
2016
Cited alongside, same era.
Kim, J., Kwon Lee, J., Mu Lee, K.: Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1646–1654 (2016)
2016
Cited alongside, same era.
Li, C., Wand, M.: Combining markov random fields and convolutional neural networks for image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2479–2486 (2016)
2016
Cited alongside, same era.
Li, C., Wand, M.: Precomputed real-time texture synthesis with markovian generative adversarial networks. In: European Conference on Computer Vision. pp. 702–716 (2016)
2016
Cited alongside, same era.
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)
2017
Later among the works it cites.
Li, Y., Wang, N., Liu, J., Hou, X.: Demystifying neural style transfer. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17. pp. 2230–2236 (2017). https://doi.org/10.24963/ijcai.2017/310, https://doi.org/10.24963/ijcai.2017/310
2017
Later among the works it cites.
Li, Y., Chen, F., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Diversified texture synthesis with feed-forward networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Later among the works it cites.
Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Universal style transfer via feature transforms. In: Advances in Neural Information Processing Systems (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Prisma Labs, I.: Prisma: Turn memories into art using artificial intelligence (2016), http://prisma-ai.com
2016
Cited alongside, same era.
Ulyanov, D., Lebedev, V., Vedaldi, A., Lempitsky, V.: Texture networks: Feed-forward synthesis of textures and stylized images. In: International Conference on Machine Learning. pp. 1349–1357 (2016)
2016
Cited alongside, same era.
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. In: International Conference on Learning Representations (2016)
2016
Cited alongside, same era.
Chen, D., Liao, J., Yuan, L., Yu, N., Hua, G.: Coherent online video style transfer. Proceedings of the IEEE International Conference on Computer Vision (2017)
2017
Cited alongside, same era.
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stylebank: An explicit representation for neural image style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Cited alongside, same era.
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
2017
Cited alongside, same era.
Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
2017
Cited alongside, same era.
Dumoulin, V., Shlens, J., Kudlur, M.: A learned representation for artistic style. In: International Conference on Learning Representations (2017)
2017
Cited alongside, same era.
Lu, M., Zhao, H., Yao, A., Xu, F., Chen, Y., Zhang, L.: Decoder network over lightweight reconstructed feature for fast semantic style transfer. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
2017
Later among the works it cites.
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Improved texture networks: Maximizing quality and diversity in feed-forward stylization and texture synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Later among the works it cites.
Wang, X., Oxholm, G., Zhang, D., Wang, Y.F.: Multimodal transfer: A hierarchical deep convolutional neural network for fast artistic style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
2017
Later among the works it cites.
Wei, Z., Sun, Y., Wang, J., Lai, H., Liu, S.: Learning adaptive receptive fields for deep image parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2434–2442 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Chen, D., Yuan, L., Liao, J., Yu, N., Hua, G.: Stereoscopic neural style transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
2018
Closest in time.
Fan, Q., Chen, D., Yuan, L., Hua, G., Yu, N., Chen, B.: Decouple learning for parameterized image operators. In: European Conference on Computer Vision (2018)
2018
Closest in time.
He, M., Chen, D., Liao, J., Sander, P.V., Yuan, L.: Deep exemplar-based colorization. ACM Transactions on Graphics (Proc. of Siggraph 2018) (2018)
2018
Closest in time.
Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., Agrawal, A.: Context encoding for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
2018
Closest in time.