The idea is that any distortion loss imposes specific artifacts. For example, MSE tends to blur outputs, CNN-feature-based losses (VGG, LPIPS) tend to produce g

The idea is that any distortion loss imposes specific artifacts. For example, MSE tends to blur outputs, CNN-feature-based losses (VGG, LPIPS) tend to produce gridding patterns or also blur. Now, when the discriminator network sees these artifacts, those artifacts very obviously distinguish the reconstructions from the input, and thus the gradients from the discriminator guide the optimization away from these artifacts. Let me know if this helps!