Adversarial Loss

• We apply Adversarial Loss to both the Generators, where the Generator tries to generate the images of it’s domain, while its corresponding discriminator distinguishes between the translated samples and real samples.
• Generator aims to minimize this loss against its corresponding Discriminator that tries to maximize it.