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[1] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville A. & Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672-2680). [2] Wei, X., Gong, B., Liu, Z., Lu, W., & Wang, L. (2018). Improving the improved training of Wasserstein Gans: A consistency term and its dual effect. arXiv preprint arXiv:1803.01541. [3] Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. [4] Choi, Y., Choi, M., Kim, M., Ha, J. W., Kim, S., & Choo, J. (2018). StarGan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8789-8797). [5] Samarzija, B., & Ribaric, S. (2014, May). An approach to the de-identification of faces in different poses. In 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (pp. 1246-1251). IEEE. [6] Wu, Y., Yang, F., & Ling, H. (2018). Privacy-Protective-GAN for Face De-identification. arXiv preprint arXiv:1806.08906. [7] Dosovitskiy, A., Tobias Springenberg, J., & Brox, T. (2015). Learning to generate chairs with convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1538-1546). [8] Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein gan. arXiv preprint arXiv:1701.07875. [9] Kellerer, H. G. (1985). Duality theorems and probability metrics. In Proceedings of the seventh conference on probability theory (Brasov, 1982) (pp. 211-220). [10] Schroff, F., Kalenichenko, D., & Philbin, J. (2015). Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition [11] LeFevre, K., DeWitt, D. J., & Ramakrishnan, R. (2006, April). Mondrian multidimensional k-anonymity. In ICDE (Vol. 6, p. 25). [12] Cootes, T. F., & Taylor, C. J. (2004). Statistical models of appearance for computer vision. [13] Meden, B., Mallı, R. C., Fabijan, S., Ekenel, H. K., Štruc, V., & Peer, P. (2017). Face deidentification with generative deep neural networks. IET Signal Processing, 11(9), 1046-1054. [14] Noh, H., Hong, S., & Han, B. (2015). Learning deconvolution network for semantic segmentation. In Proceedings of the IEEE international conference on computer vision (pp. 1520-1528). [15] Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134). [16] Odena, A. (2016). Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583. [17] P Wei, L., Zhang, S., Gao, W., & Tian, Q. (2018). Person transfer gan to bridge domain gap for person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 79-88). [18] Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. [19] Karras, T., Laine, S., & Aila, T. (2018). A style-based generator architecture for generative adversarial networks. arXiv preprint arXiv:1812.04948.
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