  Generative Adversarial Networks (GANs) have significantly advanced image
synthesis, however, the synthesis quality drops significantly given a limited
amount of training data. To improve the data efficiency of GAN training, prior
work typically employs data augmentation to mitigate the overfitting of the
discriminator yet still learn the discriminator with a bi-classification (i.e.,
real vs. fake) task. In this work, we propose a data-efficient Instance
Generation (InsGen) method based on instance discrimination. Concretely,
besides differentiating the real domain from the fake domain, the discriminator
is required to distinguish every individual image, no matter it comes from the
training set or from the generator. In this way, the discriminator can benefit
from the infinite synthesized samples for training, alleviating the overfitting
problem caused by insufficient training data. A noise perturbation strategy is
further introduced to improve its discriminative power. Meanwhile, the learned
instance discrimination capability from the discriminator is in turn exploited
to encourage the generator for diverse generation. Extensive experiments
demonstrate the effectiveness of our method on a variety of datasets and
training settings. Noticeably, on the setting of 2K training images from the
FFHQ dataset, we outperform the state-of-the-art approach with 23.5% FID
improvement.
