EviD-GAN: Improving GAN With an Infinite Set of Discriminators at Negligible Cost.

Ensemble learning improves the capability of convolutional neural network (CNN)-based discriminators, whose performance is crucial to the quality of generated samples in generative adversarial network (GAN). However, this learning strategy results in a significant increase in the number of parameters along with computational overhead. Meanwhile, the suitable number of discriminators required to enhance GAN performance is still being investigated.