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Chapter 6
To elaborate, let us say the input is X, then the generator of the first GAN performs
a mapping G: X → Y; thus its output would be Y = G(X). The generator of the
second GAN performs an inverse mapping F: Y → X, resulting in X = F(Y). Each
discriminator is trained to distinguish between real images and synthesized images.
The idea is shown as follows:
To train the combined GANs, the authors added, besides the conventional GAN
adversarial loss, a forward cycle consistency loss (left figure) and a backward cycle
consistency loss (right figure). This ensures that if an image X is given as input, then
after the two translations F(G(X)) ~ X the obtained image is the same, X (similarly the
backward cycle consistency loss ensures that? G(F(Y)) ~ Y).
Following are some of the successful image translations by CycleGANs:
Figure 5: Examples of some successful CycleGAN image translations
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