I am looking for literature recommendations regarding GANs with multiple discriminators. In particular, I am looking for examples where each discriminator has a slightly different learning objective. My thinking was that the generator sometimes is exposed to reward sparsity: i.e. its samples get constantly rejected. Having multiple objectives be optimised through multiple discriminators might help alleviate this problem to a certain extent, as it increases the chance of positive feedback from one of the discriminators. Do you know of any examples, and does GAN training with multiple discriminators generally make sense or does it make training more unstable for some reason I have not considered?

I am looking for literature recommendations regarding GANs with multiple discriminators. In particular, I am looking for examples where each discriminator has a slightly different learning objective, rather than learning on different data. My thinking was that the generator sometimes is exposed to reward sparsity: i.e. its samples get constantly rejected. Having multiple objectives be optimised through multiple discriminators might help alleviate this problem to a certain extent, as it increases the chance of positive feedback from one of the discriminators. Do you know of any examples, and does GAN training with multiple discriminators generally make sense or does it make training more unstable for some reason I have not considered?