Cycle-Consistent Adversarial Networks

This notebook demonstrates unpaired image to image translation using conditional GAN's, as described in Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, also known as CycleGAN. The paper proposes a method that can capture the characteristics of one image domain and figure out how these characteristics could be translated into another image domain, all in the absence of any paired training examples.

This notebook assumes you are familiar with Pix2Pix, which you can learn about in the Pix2Pix tutorial. The code for CycleGAN is similar, the main difference is an additional loss function, and the use of unpaired training data.

CycleGAN uses a cycle consistency loss to enable training without the need for paired data. In other words, it can translate from one domain to another without a one-to-one mapping between the source and target domain.

This opens up the possibility to do a lot of interesting tasks like photo-enhancement, image colorization, style transfer, etc.

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