# The CycleGAN Model

*Figure 7.1.3* shows the network model of the CycleGAN. The objective of the CycleGAN is to learn the function:

*y'* = *G*(*x*) (Equation 7.1.1)

That generates fake images, *y*
*'*, in the target domain as a function of the real source image, *x*. Learning is unsupervised by capitalizing only on the available real images, *x*, in the source domain and real images, *y*, in the target domain.

Unlike regular GANs, CycleGAN imposes the cycle-consistency constraint. The forward cycle-consistency network ensures that the real source data can be reconstructed from the fake target data:

*x'* = *F*(*G*(*x*)) (Equation 7.1.2)

This is done by minimizing the forward cycle-consistency *L1* loss:

(Equation 7.1.3)

The network is symmetric. The backward cycle-consistency network also attempts to reconstruct the real target data from the fake source data:

*y*
*'* = *G*(*F*(*y*)) (Equation 7.1.4)

This is done by minimizing the backward cycle-consistency *L1*...