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Gan implimentation in keras
Could you please explain what is renorm doing in the implementation of DecorelationNormalization? Thanks!
In train_one_step, when training discriminator, it loads discrimiantor_batch
and generator_batch
as follows:
discrimiantor_batch = self.dataset.next_discriminator_sample()
generator_batch = self.dataset.next_generator_sample()
, but the question is that the batch images in discrimiantor_batch
and generator_batch
are totally different, which means that the discriminator computes the true loss (using discrimiantor_batch) and fake loss (using generator_batch) using different images. It seems uncommon when training the discriminator.
Could you please explain it? Thanks.
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