CycleGAN: a GAN architecture for learning unpaired image to image transformations
Like the Doublemint Twins touting the joys of Doublemint gum, 2 GANs are surely better than 1 GAN. Especially if we package them together inside of one meta GAN module. And this is exactly what the CycleGAN architecture does. Have you ever harbored dark secrets of turning a horse into a zebra? The CycleGAN was developed to do just that. Learn how to turn a horse into a zebra. And more. Now right away you can notice a difference between the image to image transformation GAN architectures we've been discussing over the last few posts. Those last few posts described systems that learn from a database of matched input-output image pairs. And if your goal is to turn an edge representation into a nicely filled in continuous tone image, it's easy to build your database of matched input-output image pairs that your GAN system can then learn off of. Take a continuous tone image (which will be the output of the database pair entry), then run it through an edge detector algorithm.
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