  We present a new stage-wise learning paradigm for training generative
adversarial networks (GANs). The goal of our work is to progressively
strengthen the discriminator and thus, the generators, with each subsequent
stage without changing the network architecture. We call this proposed method
the RankGAN. We first propose a margin-based loss for the GAN discriminator. We
then extend it to a margin-based ranking loss to train the multiple stages of
RankGAN. We focus on face images from the CelebA dataset in our work and show
visual as well as quantitative improvements in face generation and completion
tasks over other GAN approaches, including WGAN and LSGAN.
