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I wouldn't say trivial. First, in order to use GAN training you need access not only to the adversarial discriminator, but also to it's derivatives. Second, the
by david-gpu 9y ago
I wouldn't say trivial. First, in order to use GAN training you need access not only to the adversarial discriminator, but also to it's derivatives. Second, the derivatives need to be reasonably bounded and reasonably smooth, otherwise your voice generator won't be able to converge to a successful solution.
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- backpropaganda 9y agoGANs can be trained without gradients using reinforcement learning. Even without GANs, just being able to tune hyper-parameters with such a discriminator should be a strong signal for the synthesizer.
- Houshalter 9y agoYou don't really need access to the other model. By just using your own NN as a discriminator, it will train to be very good at fooling it. And so it should be just as good at fooling other NNs.