GAN: why is too-good discriminator supposed to provide “small error”?

Reading here, I found:

If the discriminator wins by too big a margin, then the generator
can’t learn as the discriminator error is too small.

This is something I read somewhere else as well, but I can’t really get it. If the discriminator has a low loss, it means that when I give it a fake sample (with “fake” label) it gives me a low score (assuming its output is “probability of real”) with high certainty, so I can imagine that the gradient of the error will be small.

When I train the generator, I pass the same fake image, but with the “real” label. In this case, I expect that the gradient of the error should be high, since we are basically telling the discriminator that it’s making a mistake (and a big one, if the discriminator loss was low), so the error gradient should be high, and this gradient will be the one going to the generator for training.


You might find the answer in this paper “Towards principled methods for training generative adversarial networks” ( It has a part explaining why the generator’s gradient vanishes as the discriminator gets stronger.

Source : Link , Question Author : rand , Answer Author : kangzheng

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