GAN: Discriminator converges, generator learns almost nothing In my GAN, the discriminator loss goes down steadily, while the generator loss oscillates / does not converge.

I suspect this is due to the vanishing gradient problem. Theory: as the discriminator loss doesn't start out big to begin with, the generator will never be backpropagated small gradients into, and therefore it doesn't learn to generate better images.

What are some remedies to resolve this and make the generator actually work?
This is one of the bigger problems with GANs, they are usually hard to train.

This is an active area of research and it does not seem like this problem has been solved. Without additional information or code, it is hard to help, but usually different loss functions are one way to mitigate the problem.