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Unsupervised image-to-image translation aims at learning a joint distribution of images in different domains by using images from the marginal distributions in individual domains.
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Generative adversarial nets
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Improving variational inference with inverse autoregressive flow
D. P. Kingma, T. Salimans, and M. Welling · 2016
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Autoencoding beyond pixels using a learned similarity metric
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Coupled generative adversarial networks
M.-Y. Liu and O. Tuzel · 2016
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Conditional image generation with pixelcnn decoders
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Attribute2image: Conditional image generation from visual attributes
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M. Arjovsky, S. Chintala, and L. Bottou · 2017
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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T. Kim, M. Cha, H. Kim, J. Lee, and J. Kim · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
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Auxiliary deep generative models
L. Maaløe, C. K. Sønderby, S. K. Sønderby, and O. Winther · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
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The SYNTHIA Dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. Lopez · 2016
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Improved techniques for training gans
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A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb · 2017
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Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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