I’m a third-year undergraduate student in CS, focusing in ML and I’ve been reading a few papers on GANs. I was hoping someone could show me relevant papers on GAN architectures.

In particular, I have the idea that you can create more complicated GAN systems and perhaps get better results. AFAIK, most GANs are structured as a combination of a discriminator and a generator. My thinking is that you could split up the generator into multiple generators and then create an ensemble network, let’s call it a Decider. Ensembles generally train their generators on all available data and then cleverly combine the generators through techniques like boosting. Unlike the traditional ensembles, I want to train the Decider and the multiple generators adversarially. More specifically, the utility function of each generator would seek to maximize the probability it is selected by the Decider. Similarly, the Decider would seek to optimize its correct selection of a generator.

Is anyone aware of multiple adversarial levels in GANs? My google-fu hasn’t been strong enough to find anything related. I’m also wondering if there’s an obvious reason such experiment would fail to produce anything interesting. Any feedback is greatly appreciated.