I'm working on Pix2Pix an image-to-image translation GAN, and I noticed that there is an adversarial loss implemented using BCE, and a L1 loss implemented using MAE. I know L1 loss represents the difference between the predicted image and actual image, but I am not sure what does the GAN adversarial loss represent?
This is the official definition
The adversarial loss influences whether the generator model can output images that are plausible in the target domain
but the meaning is tough to understand. Is it representing the difference between the predicted probability distribution and actual probability distribution?