References
Wang X G, Tang X O. Face photo-sketch synthesis and recognition. IEEE Trans Pattern Anal Mach Intell, 2009, 31: 1955–1967
Zhang M, Wang N, Li Y, et al. Neural probabilistic graphical model for face sketch synthesis. IEEE Trans Neural Netw Learn Syst, 2019. doi: https://doi.org/10.1109/TNNLS.2019.2933590
Wang N, Gao X, Sun L, et al. Anchored neighborhood index for face sketch synthesis. IEEE Trans Circ Syst Video Technol, 2018, 28: 2154–2163
Zhang W, Wang X, Tang X. Coupled information-theoretic encoding for face photo-sketch recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011. 513–520
Khaligh-Razavi S M, Kriegeskorte N. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput Biol, 2014, 10: e1003915
Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. In: Proceedings of Annual Conference on Neural Information Processing Systems (NIPS), 2014. 2672–2680
Isola P, Zhu J, Zhou T, et al. Image-to-image translation with conditional adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5967–5976
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: Proceedings of International Conference on Medical Image Computing and Computer-assisted Intervention, 2015. 234–241
Acknowledgements
This work was supported by National Key R&D Program of China (Grant Nos. 2017YFB1402105, 2019YFC1521100), National Natural Science Foundation of China (Grant Nos. U1805264, 61573359, 61672103, 61473276, 61402040), and Natural Science Foundation of Beijing (Grant No. L182052).
Author information
Authors and Affiliations
Corresponding authors
Supplementary File
Rights and permissions
About this article
Cite this article
Chang, L., Jin, L., Weng, L. et al. Face-sketch learning with human sketch-drawing order enforcement. Sci. China Inf. Sci. 63, 219103 (2020). https://doi.org/10.1007/s11432-019-2890-8
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-019-2890-8