A Style-Aware Content Loss for Real-time HD Style Transfer

Fernie, E.: Art History and its Methods: A critical anthology. Phaidon, London (1995), p. 361

1995

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.

Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313

2006

Earlier work this paper cites.

Karayev, S., Trentacoste, M., Han, H., Agarwala, A., Darrell, T., Hertzmann, A., Winnemoeller, H.: Recognizing image style. arXiv preprint arXiv:1311.3715 (2013)

Original

2013

Earlier work this paper cites.

Shih, Y., Paris, S., Durand, F., Freeman, W.T.: Data-driven hallucination of different times of day from a single outdoor photo. ACM Transactions on Graphics (TOG) 32

2013

Earlier work this paper cites.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)

2014

Earlier work this paper cites.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Original

2014

Earlier work this paper cites.

Shih, Y., Paris, S., Barnes, C., Freeman, W.T., Durand, F.: Style transfer for headshot portraits. ACM Transactions on Graphics (TOG) 33

2014

Earlier work this paper cites.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

Original

2014

Earlier work this paper cites.

Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Advances in neural information processing systems. pp. 487–495 (2014)

2014

Earlier work this paper cites.

Gatys, L.A., Ecker, A.S., Bethge, M.: Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks. arXiv preprint arXiv:1505.07376 12

Original

2015

Earlier work this paper cites.

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 3431–3440 (2015)

2015

Earlier work this paper cites.

Mahendran, A., Vedaldi, A.: Understanding deep image representations by inverting them. In: Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR) (2015)

2015

Earlier work this paper cites.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115

2015

Earlier work this paper cites.

Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. arXiv preprint arXiv:1505.00687 (2015)

Original

2015

Earlier work this paper cites.

Bautista, M.A., Sanakoyeu, A., Tikhoncheva, E., Ommer, B.: Cliquecnn: Deep unsupervised exemplar learning. In: Advances in Neural Information Processing Systems. pp. 3846–3854 (2016)

2016

Earlier work this paper cites.

Che, T., Li, Y., Jacob, A.P., Bengio, Y., Li, W.: Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136 (2016)

Original

2016

Earlier work this paper cites.

Chen, T.Q., Schmidt, M.: Fast patch-based style transfer of arbitrary style. arXiv preprint arXiv:1612.04337 (2016)

Original

2016

Earlier work this paper cites.

Engstrom, L.: Fast style transfer. https://github.com/lengstrom/fast-style-transfer/ (2016), commit 55809f4e

2016

Cited alongside, same era.

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

Cited alongside, same era.

Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on. pp. 2414–2423. IEEE (2016)

2016

Cited alongside, same era.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)

2016

Cited alongside, same era.

Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Transactions on Graphics (TOG) 36

2017

Later among the works it cites.

Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Diversified texture synthesis with feed-forward networks. In: 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. pp. 385–395 (2017)

2017

Later among the works it cites.

Liao, J., Yao, Y., Yuan, L., Hua, G., Kang, S.B.: Visual attribute transfer through deep image analogy. arXiv preprint arXiv:1705.01088 (2017)

Original

2017

Later among the works it cites.

Luan, F., Paris, S., Shechtman, E., Bala, K.: Deep photo style transfer. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)