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Generative Adversarial Networks (GANs) typically learn a distribution of images in a large image dataset, and are then able to generate new images from this distribution.
Texture synthesis by non-parametric sampling
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MATLAB reimplementation of seam carving
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T. S. Cho, S. Avidan, and W. T. Freeman · 2010
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M. Zontak and M. Irani · 2011
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I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Deconvolution and checkerboard artifacts
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Weakly and self-supervised learning for content-aware deep image retargeting
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The role of minimal complexity functions in unsupervised learning of semantic mappings
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Image-to-image translation with conditional adversarial networks
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Least squares generative adversarial networks
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, and Z. Wang · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networkss
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Spectral normalization for generative adversarial networks
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Non-stationary texture synthesis by adversarial expansion
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