  Recent advances in generative adversarial networks (GANs) have shown
remarkable progress in generating high-quality images. However, this gain in
performance depends on the availability of a large amount of training data. In
limited data regimes, training typically diverges, and therefore the generated
samples are of low quality and lack diversity. Previous works have addressed
training in low data setting by leveraging transfer learning and data
augmentation techniques. We propose a novel transfer learning method for GANs
in the limited data domain by leveraging informative data prior derived from
self-supervised/supervised pre-trained networks trained on a diverse source
domain. We perform experiments on several standard vision datasets using
various GAN architectures (BigGAN, SNGAN, StyleGAN2) to demonstrate that the
proposed method effectively transfers knowledge to domains with few target
images, outperforming existing state-of-the-art techniques in terms of image
quality and diversity. We also show the utility of data instance prior in
large-scale unconditional image generation.
