It would be interesting to try to turn the randomness tests themselves into additional loss functions, supplementing the GAN loss. Production-ready GANs in my

It would be interesting to try to turn the randomness tests themselves into additional loss functions, supplementing the GAN loss. Production-ready GANs in my experience are trained with a lot of auxillary losses. For example: HiFi GAN (for audio generation) uses four different discriminators to detect problems in the spectrogram and fine structure of the waveform, and the losses are more about matching the discriminator features than the actual classification result. Works great, but is hilariously 'impure.'