Hands-On Deep Learning Algorithms with Python

Cycle consistency loss

The adversarial loss alone does not ensure the proper mapping of the images. For instance, a generator can map the images from the source domain to a random permutation of images in the target domain which can match the target distribution.

So, to avoid this, we introduce an additional loss called cycle consistent loss. It enforces both generators G and F to be cycle-consistent.

Let's recollect the function of the generators:

• Generator G: Converts x to y
• Generator F: Converts y to x

We know that generator G takes the source image x and converts it to a fake target image y. Now if we feed this generated fake target image y to generator F, it has to return the original source image x. Confusing, right?

Look at the following ...