Comments (1)
Hi @xygo567, that is correct, is it not?
In the adversarial loss, we give the wrong labels to the discriminator's loss function in order to obtain gradient information to change the generator's parameters in a way that pushes the discriminator to make wrong predictions. Because we want to the discriminator to be wrong (i.e. fooled by the generator's output).
from a-pytorch-tutorial-to-super-resolution.
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from a-pytorch-tutorial-to-super-resolution.