  We propose to tackle the mode collapse problem in generative adversarial
networks (GANs) by using multiple discriminators and assigning a different
portion of each minibatch, called microbatch, to each discriminator. We
gradually change each discriminator's task from distinguishing between real and
fake samples to discriminating samples coming from inside or outside its
assigned microbatch by using a diversity parameter $\alpha$. The generator is
then forced to promote variety in each minibatch to make the microbatch
discrimination harder to achieve by each discriminator. Thus, all models in our
framework benefit from having variety in the generated set to reduce their
respective losses. We show evidence that our solution promotes sample diversity
since early training stages on multiple datasets.
