Isola et al 2017.pol - guillaumedescoteauxisabelle/ma-biblio GitHub Wiki
page: 1 type: text-highlight created: 2020-11-21T14:14:49.391Z color: yellow Image-to-Image Translation with Conditional Adversarial Networks
page: 1 type: text-highlight created: 2020-11-21T14:14:54.668Z color: yellow Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros
page: 1 type: text-highlight created: 2020-11-21T14:17:30.031Z color: yellow In analogy to automatic language translation, we define automatic image-to-image translation as the problem of translating one possible rep- resentation of a scene into another, given sufficient train- ing data
page: 1 type: text-highlight created: 2020-11-21T14:19:06.912Z color: red CNNs learn to minimize a loss function – an objective that scores the quality of results – and although the learning process is automatic, a lot of manual effort still goes into designing effective losses.
page: 2 type: text-highlight created: 2020-11-27T19:13:17.960Z color: yellow It would be highly desirable if we could instead specify only a high-level goal, like “make the output indistinguish- able from reality”, and then automatically learn a loss func- tion appropriate for satisfying this goal. Fortunately, this is exactly what is done by the recently proposed Generative Adversarial Networks (GANs) [ 22 , 12 , 41 , 49 , 59 ]
page: 2 type: text-highlight created: 2020-11-27T19:19:37.119Z color: yellow GANs are generative models that learn a mapping from random noise vector z to output image y , G : z → y [ 22 ]. In contrast, conditional GANs learn a mapping from observed image x and random noise vector z , to y , G : { x, z } → y . The generator G is trained to produce outputs that cannot be distinguished from “real” images by an adversarially trained discriminator, D , which is trained to do as well as possible at detecting the generator’s “fakes”. This training procedure is diagrammed in Figure 2
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page: 3 type: comment created: 2020-11-27T19:20:47.033Z