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Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details.
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M. Ehrlich, T. J. Shields, T. Almaev, and M. R. Amer, “Facial attributes classification using multi-task representation learning,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , 2016
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A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” in International Conference on Machine Learning (ICML) , 2016
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G. Perarnau, J. van de Weijer, B. Raducanu, and J. M. Álvarez, “Invertible conditional gans for image editing,” in Advances in Neural Information Processing Systems (NIPS) Workshops , 2016
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S. Zhou, T. Xiao, Y. Yang, D. Feng, Q. He, and W. He, “Genegan: Learning object transfiguration and attribute subspace from unpaired data,” in British Machine Vision Conference (BMVC) , 2017
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G. Lample, N. Zeghidour, N. Usunier, A. Bordes, L. Denoyer, and M. Ranzato, “Fader networks: Manipulating images by sliding attributes,” in Advances in Neural Information Processing Systems (NIPS) , 2017
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P. Upchurch, J. Gardner, G. Pleiss, R. Pless, N. Snavely, K. Bala, and K. Weinberger, “Deep feature interpolation for image content changes,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
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2016
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A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” in International Conference on Learning Representations (ICLR) , 2016
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A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in Advances in Neural Information Processing Systems (NIPS) Workshops , 2016
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X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in Advances in Neural Information Processing Systems (NIPS) , 2016
2016
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2016
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2016
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W. Shen and R. Liu, “Learning residual images for face attribute manipulation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
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M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” in Advances in Neural Information Processing Systems (NIPS) , 2017
2017
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J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in IEEE International Conference on Computer Vision (ICCV) , 2017
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2017
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T. Kaneko, K. Hiramatsu, and K. Kashino, “Generative attribute controller with conditional filtered generative adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
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P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017
2017
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2017
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T. Xiao, J. Hong, and J. Ma, “Dna-gan: Learning disentangled representations from multi-attribute images,” in International Conference on Learning Representations (ICLR) Workshops , 2018
2018
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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 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018
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