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Unsupervised Image-to-Image Translation with Generative Adversarial Networks
[article]
2017
arXiv
pre-print
It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.), while keeping the original structure or semantics. We define this requirement as the "image-to-image translation" problem, and propose a general approach to achieve it, based on deep convolutional and conditional generative adversarial networks (GANs), which has gained a phenomenal success to learn mapping images from noise input since 2014. In this work, we develop a
arXiv:1701.02676v1
fatcat:cy2l42j6pfhxfgefumbooc3ure