References:

[1] P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with
conditional adversarial networks,” in Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 1125–1134, 2017.
[2] A. A. Efros and W. T. Freeman, “Image quilting for texture synthesis and trans-
fer,” in Proceedings of the 28th annual conference on Computer graphics and
interactive techniques, pp. 341–346, ACM, 2001.
[3] A. Hertzmann, C. E. Jacobs, N. Oliver, B. Curless, and D. H. Salesin, “Image
analogies,” in Proceedings of the 28th Annual Conference on Computer Graphics
and Interactive Techniques, pp. 327–340, ACM, 2001.
[4] A. Buades, B. Coll, and J.-M. Morel, “A non-local algorithm for image denois-
ing,” in IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, vol. 2, pp. 60–65, IEEE, 2005.
[5] D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels
with a common multi-scale convolutional architecture,” in Proceedings of the
IEEE International Conference on Computer Vision, pp. 2650–2658, 2015.
[6] R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European
Conference on Computer Vision, pp. 649–666, Springer, 2016.
[7] Y. Shih, S. Paris, F. Durand, and W. T. Freeman, “Data-driven hallucination
of different times of day from a single outdoor photo,” ACM Transactions on
Graphics, vol. 32, no. 6, p. 200, 2013.
[8] P.-Y. Laffont, Z. Ren, X. Tao, C. Qian, and J. Hays, “Transient attributes for
high-level understanding and editing of outdoor scenes,” ACM Transactions on
Graphics, vol. 33, no. 4, p. 149, 2014.
[9] S. Xie and Z. Tu, “Holistically-nested edge detection,” in Proceedings of the IEEE
International Conference on Computer Vision, pp. 1395–1403, 2015.
[10] T. Chen, M.-M. Cheng, P. Tan, A. Shamir, and S.-M. Hu, “Sketch2photo: In-
ternet image montage,” in ACM transactions on graphics, vol. 28, p. 124, ACM,
2009.
[11] L. A. Gatys, A. S. Ecker, and M. Bethge, “A neural algorithm of artistic style,”
arXiv preprint arXiv:1508.06576, 2015.
[12] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image transla-
tion using cycle-consistent adversarial networks,” 2017 IEEE International Con-
ference on Computer Vision, Oct 2017.
[13] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale
image recognition,” arXiv preprint arXiv:1409.1556, 2014.
[14] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair,
A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neu-
ral Information Processing Systems (Z. Ghahramani, M. Welling, C. Cortes,
N. D. Lawrence, and K. Q. Weinberger, eds.), pp. 2672–2680, Curran Associates,
Inc., 2014.
[15] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. The MIT Press,
2016.
[16] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical
Learning, vol. 112. Springer, 2013.
[17] A. Géron, Hands-on machine learning with Scikit-Learn and TensorFlow: con-
cepts, tools, and techniques to build intelligent systems. ” O’Reilly Media, Inc.”,
2017.
[18] C. M. Bishop et al., Neural Networks for Pattern Recognition. Oxford university
press, 1995.
[19] S. Haykin and N. Network, “A comprehensive foundation,” Neural networks,
vol. 2, no. 2004, p. 41, 2004.
[20] C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, “Activation functions:
Comparison of trends in practice and research for deep learning,” arXiv preprint
arXiv:1811.03378, 2018.
[21] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal repre-
sentations by error propagation,” tech. rep., California Univ San Diego La Jolla
Inst for Cognitive Science, 1985.
[22] P. Werbos, “Beyond regression:” new tools for prediction and analysis in the
behavioral sciences,” Ph. D. dissertation, Harvard University, 1974.
[23] D. H. Hubel and T. N. Wiesel, “Receptive fields and functional architecture of
monkey striate cortex,” The Journal of physiology, vol. 195, no. 1, pp. 215–243,
1968.
[24] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al., “Gradient-based learning
applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11,
pp. 2278–2324, 1998.
[25] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep
convolutional neural networks,” in Advances in neural information processing
systems, pp. 1097–1105, 2012.
[26] C.-C. J. Kuo, “Understanding convolutional neural networks with a mathemati-
cal model,” Journal of Visual Communication and Image Representation, vol. 41,
pp. 406–413, 2016.
[27] V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann
machines,” in Proceedings of the 27th international conference on machine learn-
ing (ICML-10), pp. 807–814, 2010.
[28] J. R. Gardner, P. Upchurch, M. J. Kusner, Y. Li, K. Q. Weinberger, K. Bala, and
J. E. Hopcroft, “Deep manifold traversal: Changing labels with convolutional
features,” arXiv preprint arXiv:1511.06421, 2015.
[29] C. Li and M. Wand, “Combining markov random fields and convolutional neu-
ral networks for image synthesis,” in Proceedings of the IEEE Conference on
Computer Vision and Pattern Recognition, pp. 2479–2486, 2016.
[30] A. Selim, M. Elgharib, and L. Doyle, “Painting style transfer for head portraits
using convolutional neural networks,” ACM Transactions on Graphics, vol. 35,
no. 4, p. 129, 2016.
[31] Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified
generative adversarial networks for multi-domain image-to-image translation,”
in Proceedings of the IEEE Conference on Computer Vision and Pattern Recog-
nition, pp. 8789–8797, 2018.
[32] S. Zhu, R. Urtasun, S. Fidler, D. Lin, and C. Change Loy, “Be your own prada:
Fashion synthesis with structural coherence,” in Proceedings of the IEEE Inter-
national Conference on Computer Vision, pp. 1680–1688, 2017.
[33] L. A. Gatys, A. S. Ecker, and M. Bethge, “Image style transfer using convolu-
tional neural networks,” in Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, pp. 2414–2423, 2016.
[34] H. Zhang and K. Dana, “Multi-style generative network for real-time transfer,”
arXiv preprint arXiv:1703.06953, 2017.
[35] D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Instance normalization: The missing
ingredient for fast stylization,” arXiv preprint arXiv:1607.08022, 2016.
[36] T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida, “Spectral normalization
for generative adversarial networks,” arXiv preprint arXiv:1802.05957, 2018.
[37] S. Zhang and D. Yang, “Pet hair color transfer based on cyclegan,” in 2018 5th
International Conference on Systems and Informatics (ICSAI), pp. 998–1004,
IEEE, 2018.
[38] R. Longman and R. Ptucha, “Embedded cyclegan for shape-agnostic image-to-
image translation,” in 2019 IEEE International Conference on Image Processing
(ICIP), pp. 969–973, Sep. 2019.
[39] J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer
and super-resolution,” in European Conference on Computer Vision, pp. 694–
711, Springer, 2016.
[40] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recog-
nition,” in Proceedings of the IEEE conference on Computer Vision and Pattern
Recognition, pp. 770–778, 2016.
[41] C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta,
A. Aitken, A. Tejani, J. Totz, Z. Wang, et al., “Photo-realistic single image
super-resolution using a generative adversarial network,” in Proceedings of the
IEEE conference on Computer Vision and Pattern Recognition, pp. 4681–4690,
2017.
[42] C. Li and M. Wand, “Precomputed real-time texture synthesis with markovian
generative adversarial networks,” in European Conference on Computer Vision,
pp. 702–716, Springer, 2016.
[43] M. E. Celebi, Q. Wen, S. Hwang, and G. Schaefer, “Color quantization of der-
moscopy images using the k-means clustering algorithm,” in Color Medical Image
Analysis, pp. 87–107, Springer, 2013.
[44] S. Lloyd, “Least squares quantization in pcm,” IEEE transactions on information
theory, vol. 28, no. 2, pp. 129–137, 1982.
[45] E. Forgy, “Cluster analysis of multivariate data: Efficiency versus interpretability
of classification,” Biometrics, vol. 21, no. 3, pp. 768–769, 1965.
[46] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv
preprint arXiv:1412.6980, 2014.
[47] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Im-
proved training of wasserstein gans,” in Advances in Neural Information Pro-
cessing Systems, pp. 5767–5777, 2017.