참고문헌

[1] Diederik P. Kingma, and Max Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013.
[2] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville and Yoshua Bengio, “Generative adversarial nets,” Advances in neural information processing systems, 2014.
[3] Hou, X., Shen, L., Sun, K., and Qiu, G. “Deep feature consistent variational Auto-encoder,” Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on. IEEE, 2017.
[4] Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H., “Generative adversarial text to image synthesis,” arXiv preprint arXiv:1605.05396, 2016.
[5] Zhang, H., Xu, T., Li, H., Zhang, S., Huang, X., Wang, X., and Metaxas, D. “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” arXiv preprint, 2017.
[6] Kim, Jiwon, Jung Kwon Lee, and Kyoung Mu Lee., “Accurate image super-resolution using very deep convolutional networks,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
[7] He, K., Zhang, X., Ren, S., and Sun, J., “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
[8] Dong, C., Loy, C. C., He, K., and Tang, X., “Learning a deep convolutional network for image super-resolution,” European conference on computer vision. Springer, Cham, 2014.
[9] He, K., Zhang, X., Ren, S., and Sun, J., “Deep residual learning for image recognition,” Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
[10] Quintana, V. H., and Davison, E. J., “Clipping-off gradient algorithms to compute optimal controls with constrained magnitude,” International Journal of Control, vol. 20, no. 2, pp. 243-255, 1974.
[11] Hinton, Geoffrey E., and Ruslan R. Salakhutdinov., “Reducing the dimensionality of data with neural networks,” science vol. 313, iss. 5786, pp. 504-507, 2006.
[12] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton., “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, 2012.
[13] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” Computer Vision and Pattern Recognition(CVPR) Proceedings of the 2001 IEEE Computer Society Conference on, 2001.
[14] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Computer Vision and Pattern Recognition(CVPR) IEEE Computer Society Conference on, vol. 1, pp. 886-893, 2005.
[15] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision, vol. 60, no. 2, pp. 91-110, 2004.
[16] Zeiler, M. D., Krishnan, D., Taylor, G. W., and Fergus, R. “Deconvolutional networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), 2010.
[17] Hawkins, Douglas M. “The problem of overfitting,” Journal of chemical information and computer sciences 44.1 (2004): 1-12.
[18] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng, Google Brain., “Tensorflow: a system for large-scale machine learning,” OSDI, Vol. 16, 2016.
[19] Chollet, François. “Keras: Deep learning library for theano and tensorflow,” URL: https://keras.io/k, 2015.
[20] Liu, Ziwei, et al. “Large-scale CelebFaces Attributes (CelebA) Dataset,” URL: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html, 2018.
[21] Viola, Paul, and Michael J. Jones., “Robust real-time face detection,” International journal of computer vision, vol. 57, no. 2, pp. 137-154, 2004.
[22] Barratt, Shane, and Rishi Sharma., “A Note on the Inception Score,” arXiv preprint arXiv:1801.01973, 2018.
[23] C. Szegedy, et al, “Rethinking the inception architecture for computer vision,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp.2818-2826, 2016.
[24] Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z., “Going deeper with convolutions,” Proceedings of the IEEE conference on Computer Vision and Pattern Recognition(CVPR), pp.1-9, 2015.
[25] Odena, Augustus, Christopher Olah, and Jonathon Shlens., “Conditional image synthesis with auxiliary classifier gans,” arXiv preprint arXiv:1610.09585, 2016.
[26] Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H., “Generative adversarial text to image synthesis,” arXiv preprint arXiv:1605.05396, 2016.
[27] Hastings, W. Keith., “Monte Carlo sampling methods using Markov chains and their applications,” Oxford University Press on behalf of Biometrika Trust, Biometrika, vol. 57, no. 1, pp. 97-109, 1970.
[28] Isola, P., Zhu, J. Y., Zhou, T., and Efros, A. A., “Image-to-image translation with conditional adversarial networks,” arXiv preprint , 2017.