  Generative adversarial networks (GANs) typically require ample data for
training in order to synthesize high-fidelity images. Recent studies have shown
that training GANs with limited data remains formidable due to discriminator
overfitting, the underlying cause that impedes the generator's convergence.
This paper introduces a novel strategy called Adaptive Pseudo Augmentation
(APA) to encourage healthy competition between the generator and the
discriminator. As an alternative method to existing approaches that rely on
standard data augmentations or model regularization, APA alleviates overfitting
by employing the generator itself to augment the real data distribution with
generated images, which deceives the discriminator adaptively. Extensive
experiments demonstrate the effectiveness of APA in improving synthesis quality
in the low-data regime. We provide a theoretical analysis to examine the
convergence and rationality of our new training strategy. APA is simple and
effective. It can be added seamlessly to powerful contemporary GANs, such as
StyleGAN2, with negligible computational cost.
