For normal supervised learning the dataset is split in train and test (let's keep it simple).
Generative Adversarial Networks are unsupervised learning but there is a supervised loss function in the discriminator.
Does it make sense to split the data into train and test when training GANs?
My first opinion will be no, but I am not 100% sure. Is there any reason why having a test set will help the Generator?