  Generative Adversarial Networks (GANs) have achieved remarkable achievements
in image synthesis. These successes of GANs rely on large scale datasets,
requiring too much cost. With limited training data, how to stable the training
process of GANs and generate realistic images have attracted more attention.
The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three
aspects: (i) Mismatch Between Training and Target Distributions, (ii)
Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data
Spaces. Although many augmentation and pre-training strategies have been
proposed to alleviate these issues, there lacks a systematic survey to
summarize the properties, challenges, and solutions of DE-GANs. In this paper,
we revisit and define DE-GANs from the perspective of distribution
optimization. We conclude and analyze the challenges of DE-GANs. Meanwhile, we
propose a taxonomy, which classifies the existing methods into three
categories: Data Selection, GANs Optimization, and Knowledge Sharing. Last but
not the least, we attempt to highlight the current problems and the future
directions.
