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Unfortunately I don't think there is an easy way of doing this. It all comes down to this: you need some way to evaluate how good your GAN is, which isn't an easy task. Why not use the discriminator? The way evaluation is done during training is through the discriminator. So, one first thought would be to hold out one part of the training samples, generate ...


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The development set, in other words, validation set or holdout set is like executing cross-validation for one fold only. It's faster, therefore cheaper. Typically, cross-validation is more robust because the performance is averaged across several tests instead of one. This might make sense when dealing with smaller datasets. When, data is not a problem, ...


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