Transcription of Labels to Street Scene Labels to Facade BW to Color
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Labels to Street Scene Labels to Facade BW to Color
Image-to-Image Translation with conditional Adversarial NetworksPhillip IsolaJun-Yan ZhuTinghui ZhouAlexei A. EfrosBerkeley AI Research (BAIR) Laboratory, UC to FacadeBW to ColorAerial to MapLabels to Street SceneEdges to Photoinputoutputinputinputinputinputoutp utoutputoutputoutputinputoutputDay to NightFigure 1: Many problems in image processing, graphics, and vision involve translating an input image into a corresponding output problems are often treated with application-specific algorithms, even though the setting is always the same: map pixels to adversarial nets are a general-purpose solution that appears to work well on a wide variety of these problems. Here we showresults of the method on several. In each case we use the same architecture and objective, and simply train on different investigate conditional adversarial networks as ageneral-purpose solution to image-to-image translationproblems.
contrast, conditional GANs learn a mapping from observed image xand random noise vector z, to y, G: fx;zg!y. The generator Gis trained to produce outputs that cannot be
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