[Discussion]Why CycleGAN does not need randomness as input?
I understand that CycleGAN is trying to learn a deterministic mapping between the two domains. However, in the case of conditional GAN (cGAN), if there is no randomness in the generator, the discriminator will get overfitting, and consequently generator will not improve (as discussed in the following link).
Why the same overfitting problem does not cause trouble to CycleGAN? Is it between the input dataset to CycleGAN is versatile enough to prevent discriminator from overfitting?