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Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data.
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Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
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Fast and accurate matrix completion via truncated nuclear norm regularization
Y. Hu, D. Zhang, J. Ye, X. Li, and X. He · 2013
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3D object representations for fine-grained categorization
J. Krause, M. Stark, J. Deng, and L. Fei-Fei · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Image completion using planar structure guidance
J.-B. Huang, S. B. Kang, N. Ahuja, and J. Kopf · 2014
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Inceptionism: Going deeper into neural networks
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Shepard convolutional neural networks
J. S. Ren, L. Xu, Q. Yan, and W. Sun · 2015
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Attribute2Image: Conditional image generation from visual attributes
X. Yan, J. Yang, K. Sohn, and H. Lee · 2015
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Image style transfer using convolutional neural networks
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Auto-encoding variational bayes
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Deep generative image models using a Laplacian pyramid of adversarial networks
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Inverting visual representations with convolutional networks
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Texture synthesis using convolutional neural networks
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Context encoders: Feature learning by inpainting
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