cycle consistency loss explanation
In normal image GANs, the generator receives random noise and generates an image. As you can see, there is no &quot;visual relationship&quot; between the input and output.
In cycle GANs, generators receive an image and generate another image. The idea is that the input image is of domain A and the output image is a &quot;translation&quot; of the input image into domain B. However, with the experience with normal GANs, why would the &quot;translated image&quot; have any visual relationship with the original one? If the cycle consistency loss were not there, a generator could simply generate images in the target domain that were totally unrelated to the input and the discriminator would be Ok with it.
The role of the cycle consistency loss is to ensure that the generated output image is actually a version of the input image where the domain is what changes, but the &quot;contents&quot; are kept.