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  1. Oct 2019
    1. So we train the generator with the following procedure: Sample random noise. Produce generator output from sampled random noise. Get discriminator "Real" or "Fake" classification for generator output. Calculate loss from discriminator classification. Backpropagate through both the discriminator and generator to obtain gradients. Use gradients to change only the generator weights.

      GAN- Training for both generator and discriminator as a whole