Help interpreting GAN output, and how to fix it?
In theory, we should first train the discriminator to optimal. However, if it becomes too good, then it will reject fake data every time, thus making the generator unable to learn. Unlike other ML task, lower GAN losses does not mean the training is converging. Newer GANs have better techniques to make training GAN easier such as Wasserstein loss, spectral normalization and progressive growing.