I suppose the reason for this is that if you apply BN in the discriminator for both real and fake images, the learned BN variables will aggregate both distributions, which could break or slow down the training since fake images do not contain any useful information at the beginning of the training and their distribution will change over the time, so it will be challenging for the discriminator to train.
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