Abstract
Generative Adversarial Networks (GANs) are a
powerful class of deep generative models. In this
paper, we extend GAN to the problem of generating data that are not only close to a primary data
source but also required to be different from auxiliary data sources. For this problem, we enrich
both GAN’s formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We
term our method Push-and-Pull GAN (P2GAN).
We conduct extensive experiments to demonstrate
the merit of P2GAN in two applications: generating data with constraints and addressing the mode
collapsing problem. We use CIFAR-10, STL-10,
and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN’s effectiveness in
addressing the mode collapsing problem. The results show that P2GAN outperforms the state-ofthe-art baselines. For the problem of generating
data with constraints, we show that P2GAN can
successfully avoid generating specific features such
as black hair