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Good morning everybody. In most of my videos so far Iamp;#39;ve been discussing GANs which can generate images. Although generating images is really awesome GANs have applications far beyond just that. Today Iamp;#39;d like to talk about how GANs can be applied to the translation of images. Specifically Iamp;#39;ll be talking about CycleGAN and how itamp;#39;s able to translate images of one kind to another without needing pairs of images. In some cases like the one youamp;#39;re seeing here images can be split up into two distinct domains. Here the domains are satellite images and map images. Letamp;#39;s say we wanted to make an AI which translates satellite images into map images. The simplest approach would be relatively straightforward; we could simply train an autoencoder to translate the photo, then compare the translated photo to its actual pair. The autoencoder would use some convolution blocks, down sampling the image a few times then up sampling a few times to return t